A laser radar-based remote control method and system

Abstract

The application discloses a kind of remote control method and system based on laser radar, method includes: by pre-scanning obtains current dust concentration, obtains the double-pulse emission parameter of different intensity according to dust concentration;Double-pulse is emitted to each scanning direction and echo waveform is collected;According to echo waveform, generate three-dimensional point cloud, extract echo intensity, pulse broadening degree, energy distribution concentration degree and the point correlation in point cloud time domain feature;Based on point cloud spatial distribution and time domain feature, identify suspected obstacle target, calculate intensity proportion deviation, broadening proportion deviation and energy distribution deviation and weighted fusion obtains comprehensive discriminant score, when score exceeds threshold value, combined with spatial geometric consistency verification and time sequence stability verification determine target as multipath artifact or real obstacle;And based on determination result makes corresponding processing.The application can effectively distinguish real obstacle and multipath artifact, improve the reliability and safety of remote control under dust environment.

Description

Technical Field

[0001] This invention relates to the field of lidar technology, and in particular to a remote control method and system based on lidar. Background Technology

[0002] LiDAR (Light Detection and Ranging) is an active 3D environmental sensing sensor that acquires information such as the distance, orientation, and reflection intensity of target objects by emitting laser pulses and receiving echoes, generating high-precision 3D point cloud data. Remote control technology based on LiDAR involves deploying LiDAR on remote operating platforms (such as unmanned mining trucks and remotely controlled construction machinery), transmitting the sensing data wirelessly to a remote control terminal, where operators or intelligent algorithms make decisions and issue control commands, enabling precise human control of remote equipment. This technology has broad application prospects in dangerous or harsh environments such as mining, construction engineering, and port logistics.

[0003] In high-dust environments such as mines, suspended dust particles cause significant Mie scattering of laser light, leading to attenuation of the echo signal and increased near-field noise. A more insidious and dangerous problem is the creation of false targets through multipath reflection—the "multipath artifact." When a laser beam encounters both a dust cloud and a highly reflective surface (such as a flooded road surface, metal equipment panels, or reflective signs), the laser may propagate along a zigzag path of "dust scattering—high-reflectivity surface reflection—dust scattering," creating false targets that do not actually exist in the point cloud. Experimental data shows that multipath artifacts can induce false alarm rates as high as 65%, causing remote control systems to frequently trigger unnecessary obstacle avoidance maneuvers. In severe cases, they can even misjudge real obstacles as penetrable artifacts, resulting in collisions.

[0004] In existing technologies, the optimization of lidar perception in dusty environments mainly employs statistical filtering to remove noise, multi-echo detection to penetrate dust, or fusion of millimeter-wave radar for redundant sensing. However, statistical filtering struggles to distinguish between near-field high-density dust and real targets; multi-echo detection fails when the dust concentration exceeds a critical value due to the disappearance of the second echo; and multi-sensor fusion increases hardware costs. More importantly, existing technologies cannot effectively distinguish between real obstacles and multipath artifacts—both appear as objects with continuous shapes and reliable intensity in point clouds, making them difficult to differentiate using only spatial geometric information.

[0005] Therefore, accurately distinguishing between real obstacles and false targets, and improving the reliability and safety of remote control in dusty environments, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] In view of the aforementioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a remote control method and system based on lidar, which aims to effectively distinguish between real obstacles and multipath artifacts, and improve the reliability and safety of remote control in dusty environments.

[0007] To achieve the above objectives, the first aspect of the present invention discloses a remote control method based on lidar, the method comprising: Step S1: Obtain the current dust concentration at the work site through pre-scanning of the lidar; obtain the dual-pulse emission parameters based on the current dust concentration; wherein, the dual-pulse emission parameters include the intensity and pulse interval of the first laser pulse and the second laser pulse, and the intensity of the first laser pulse and the intensity of the second laser pulse are different; Step S2: Based on the dual-pulse emission parameters, emit the first laser pulse and the second laser pulse in each scanning direction, and acquire the first echo waveform corresponding to the first laser pulse and the second echo waveform corresponding to the second laser pulse; Step S3: Generate a three-dimensional point cloud based on the first echo waveform and the second echo waveform, and extract at least several time-domain features from the first echo waveform and the second echo waveform, including echo intensity, pulse broadening degree, and energy distribution concentration, and associate the extracted time-domain features as feature vectors with the corresponding points in the three-dimensional point cloud; Step S4: Based on the spatial distribution of the 3D point cloud and the temporal characteristics of each point in the 3D point cloud, identify suspected obstacle targets; based on the temporal characteristics of the first echo waveform and the second echo waveform of the corresponding point of the suspected obstacle target, calculate the intensity ratio deviation, the broadening ratio deviation, and the energy distribution deviation, and weight and fuse the three to obtain a comprehensive discrimination score; when the comprehensive discrimination score exceeds a first threshold, combine spatial geometric consistency verification and temporal stability verification to make a comprehensive judgment and confirm that the suspected obstacle target is a multipath artifact or a real obstacle; Step S5: If the suspected obstacle target is determined to be a multipath artifact, the target is filtered out from the 3D point cloud and artifact identification information is sent to the remote operation terminal; if the suspected obstacle target is determined to be a real obstacle, the target information is retained, and obstacle avoidance commands or alarm information are generated based on its position and motion state for control.

[0008] Optionally, obtaining the dual-pulse emission parameters based on the current dust concentration in step S1 includes: Based on the current dust concentration, the intensities of the first laser pulse and the second laser pulse are obtained; wherein, the higher the current dust concentration, the greater the difference in intensity between the first laser pulse and the second laser pulse; Based on the respective intensities of the first laser pulse and the second laser pulse, the pulse interval of the first laser pulse and the second laser pulse is determined to ensure that the first echo waveform and the second echo waveform do not interfere with each other.

[0009] Optionally, in step S4, The intensity ratio deviation is the actual ratio of the echo intensity extracted from the first echo waveform and the echo intensity extracted from the second echo waveform at the point corresponding to the suspected obstacle target, divided by the absolute value of the difference between the emission intensity ratio of the first laser pulse and the second laser pulse, and the emission intensity ratio. The deviation of the pulse width ratio is the absolute value of the difference between 1 and the ratio of the pulse width ratio extracted from the first echo waveform at the point corresponding to the suspected obstacle target. The energy distribution deviation is the absolute value of the difference between the energy distribution concentration extracted from the first echo waveform and the energy distribution concentration extracted from the second echo waveform at the point corresponding to the suspected obstacle target.

[0010] Optionally, the spatial geometric consistency verification in step S4 includes: Points with echo intensity higher than a preset intensity threshold are extracted from the three-dimensional point cloud, and the extracted points are clustered into high reflectivity surface candidates. For the suspected obstacle target, traverse each high reflectivity surface in the candidate high reflectivity surfaces and determine whether there is a high reflectivity surface such that the zigzag optical path from the lidar through the high reflectivity surface to the suspected obstacle target is consistent with the actual distance measured by the lidar and satisfies the law of reflection. If it exists, the high reflectivity surface is identified as the multipath reflectivity surface of the suspected obstacle target, and the confidence that the suspected obstacle target is a multipath artifact is increased; if it does not exist, the confidence that the suspected obstacle target is a multipath artifact is decreased.

[0011] Optionally, the timing stability verification in step S4 includes: Multi-frame trajectory tracking is established for the suspected obstacle target, and its position drift amplitude is calculated. If the drift amplitude exceeds the preset position drift threshold, it is determined to be a multipath artifact; otherwise, it is determined to be a real obstacle. The position drift threshold is calibrated according to the motion characteristics of the working platform.

[0012] The second aspect of the present invention discloses a remote control system based on lidar, the system comprising: a parameter acquisition module, a scanning module, a three-dimensional point cloud establishment module, an obstacle judgment module, and a control module; The parameter acquisition module is used to obtain the current dust concentration at the work site through pre-scanning of the lidar; and to obtain dual-pulse emission parameters based on the current dust concentration; wherein the dual-pulse emission parameters include the intensity and pulse interval of the first laser pulse and the second laser pulse, and the intensity of the first laser pulse and the intensity of the second laser pulse are different; The scanning module is used to emit the first laser pulse and the second laser pulse in each scanning direction based on the dual-pulse emission parameters, and to acquire the first echo waveform corresponding to the first laser pulse and the second echo waveform corresponding to the second laser pulse. The three-dimensional point cloud building module is used to generate a three-dimensional point cloud based on the first echo waveform and the second echo waveform, and extract multiple time-domain features from the first echo waveform and the second echo waveform, including at least echo intensity, pulse broadening degree, and energy distribution concentration, and associate the extracted time-domain features as feature vectors with the corresponding points in the three-dimensional point cloud. The obstacle detection module is used to identify suspected obstacle targets based on the spatial distribution of the three-dimensional point cloud and the temporal characteristics of each point in the three-dimensional point cloud; based on the temporal characteristics of the first echo waveform and the second echo waveform of the corresponding point of the suspected obstacle target, it calculates the intensity ratio deviation, the broadening ratio deviation, and the energy distribution deviation, and weights and fuses the three to obtain a comprehensive discrimination score; when the comprehensive discrimination score exceeds a first threshold, it combines spatial geometric consistency verification and temporal stability verification to make a comprehensive judgment to confirm that the suspected obstacle target is a multipath artifact or a real obstacle; The control module is used to filter out the suspected obstacle from the 3D point cloud and send the artifact identification information to the remote operation terminal if the suspected obstacle is determined to be a multipath artifact; if the suspected obstacle is determined to be a real obstacle, the target information is retained and obstacle avoidance instructions or alarm information are generated based on its position and motion state for control.

[0013] Optionally, the parameter acquisition module is specifically used for: Based on the current dust concentration, the intensities of the first laser pulse and the second laser pulse are obtained; wherein, the higher the current dust concentration, the greater the difference in intensity between the first laser pulse and the second laser pulse; Based on the respective intensities of the first laser pulse and the second laser pulse, the pulse interval of the first laser pulse and the second laser pulse is determined to ensure that the first echo waveform and the second echo waveform do not interfere with each other.

[0014] Optionally, the intensity ratio deviation is the actual ratio of the echo intensity extracted from the first echo waveform and the echo intensity extracted from the second echo waveform at the point corresponding to the suspected obstacle target, divided by the absolute value of the difference between the emission intensity ratio of the first laser pulse and the second laser pulse, and the emission intensity ratio. The deviation of the pulse width ratio is the absolute value of the difference between 1 and the ratio of the pulse width ratio extracted from the first echo waveform at the point corresponding to the suspected obstacle target. The energy distribution deviation is the absolute value of the difference between the energy distribution concentration extracted from the first echo waveform and the energy distribution concentration extracted from the second echo waveform at the point corresponding to the suspected obstacle target.

[0015] Optionally, the spatial geometric consistency verification of the obstacle determination module specifically includes: Points with echo intensity higher than a preset intensity threshold are extracted from the three-dimensional point cloud, and the extracted points are clustered into high reflectivity surface candidates. For the suspected obstacle target, traverse each high reflectivity surface in the candidate high reflectivity surfaces and determine whether there is a high reflectivity surface such that the zigzag optical path from the lidar through the high reflectivity surface to the suspected obstacle target is consistent with the actual distance measured by the lidar and satisfies the law of reflection. If it exists, the high reflectivity surface is identified as the multipath reflectivity surface of the suspected obstacle target, and the confidence that the suspected obstacle target is a multipath artifact is increased; if it does not exist, the confidence that the suspected obstacle target is a multipath artifact is decreased.

[0016] Optionally, the timing stability verification of the obstacle determination module specifically includes: Multi-frame trajectory tracking is established for the suspected obstacle target, and its position drift amplitude is calculated. If the drift amplitude exceeds the preset position drift threshold, it is determined to be a multipath artifact; otherwise, it is determined to be a real obstacle. The position drift threshold is calibrated according to the motion characteristics of the working platform.

[0017] The beneficial effects of this invention are as follows: 1. This invention actively emits dual-pulse sequences with different intensities and extracts multiple temporal features such as echo intensity ratio, pulse broadening, and energy distribution concentration for weighted fusion. This fundamentally stimulates the physical differences in nonlinear responses between real obstacles and multipath artifacts, solving the problem that existing technologies cannot distinguish between the two by relying solely on spatial geometric information, and significantly improving the accuracy of target recognition. 2. This invention introduces spatial geometric consistency verification and temporal stability verification as a dual verification mechanism. Utilizing the geometric constraint that multipath artifacts must pass through a high-reflectivity surface and their unstable temporal characteristics, the preliminary judgment results are cross-verified, effectively reducing the false judgment rate and avoiding the risk of frequent false stops caused by multipath artifacts or collisions caused by missed detection of real obstacles. 3. This invention is entirely based on the characteristics of lidar itself, without the need for additional hardware such as millimeter-wave radar or polarization cameras. It can be deployed through software upgrades, improving perception reliability while controlling system costs, and has good economic efficiency and feasibility. 4. This invention dynamically adjusts the dual-pulse emission parameters and discrimination threshold according to the dust concentration, enabling the system to adapt to different dust concentrations. It maintains high-efficiency detection under light dust conditions and enhances the ability to distinguish between genuine and counterfeit products under heavy dust conditions, thus ensuring the stability and reliability of remote control in complex and harsh environments.

[0018] In summary, this invention utilizes active double-pulse coding to elicit the nonlinear response differences between real targets and multipath artifacts. Combined with multi-dimensional temporal feature fusion, spatial geometric consistency verification, and temporal stability verification, it achieves accurate identification and filtering of multipath artifacts. This method requires no additional hardware and can adaptively adjust detection parameters based on dust concentration. It effectively solves the technical challenge of distinguishing between real obstacles and false targets in dusty environments, significantly improving the perception reliability and operational safety of remote control systems. It is suitable for unmanned remote control in high-dust environments such as mines and tunnels. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a remote control method based on lidar according to a specific embodiment of the present invention. Figure 2 This is a schematic diagram of a remote control system based on lidar provided in a specific embodiment of the present invention. Detailed Implementation

[0020] This invention discloses a remote control method and system based on lidar. Those skilled in the art can refer to the content of this document and appropriately modify the technical details to implement it. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The method and application of this invention have been described through preferred embodiments. Those skilled in the art can obviously modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to implement and apply the technology of this invention.

[0021] This invention provides a remote control method based on lidar, such as... Figure 1 As shown, the method includes: Step S1: Obtain the current dust concentration at the work site through pre-scanning with lidar; obtain the dual-pulse emission parameters based on the current dust concentration.

[0022] The dual-pulse emission parameters include the intensity and pulse interval of the first laser pulse and the second laser pulse, and the intensity of the first laser pulse and the intensity of the second laser pulse are different.

[0023] It should be noted that before or during operation, the lidar first performs a pre-scan to estimate the dust concentration in the environment by analyzing the characteristics of the echo signals (such as the number of echoes, intensity distribution, and pulse broadening). Accurate dust concentration is fundamental for subsequent adaptive adjustments. Based on the estimated dust concentration, the system dynamically sets the dual-pulse emission parameters, including the intensity of the first and second laser pulses and the time interval between them. The two pulses are set to different intensities, with the difference in intensity increasing as the dust concentration increases. This is done to achieve sufficient penetration in dusty environments using strong pulses, while using weak pulses as a reference to create conditions for distinguishing real targets from multipath artifacts through differences in nonlinear response. The pulse interval setting ensures that the two echoes do not overlap in time, facilitating subsequent waveform analysis.

[0024] In this specific embodiment, step S1, based on the current dust concentration, obtains the dual-pulse emission parameters, including: Based on the current dust concentration, the intensities of the first and second laser pulses are obtained; the higher the current dust concentration, the greater the difference in intensity between the first and second laser pulses. The pulse interval between the first laser pulse and the second laser pulse is determined based on their respective intensities to ensure that the first echo waveform and the second echo waveform do not interfere with each other.

[0025] It should be noted that in step S1, the specific method for obtaining the dual-pulse emission parameters based on the dust concentration is as follows: First, based on the dust concentration estimated by the pre-scan, the intensity values ​​of the first laser pulse and the second laser pulse are determined respectively. The higher the dust concentration, the greater the intensity difference between the two pulses is set; that is, the stronger pulse is stronger to enhance penetration ability, and the weaker pulse is weaker to amplify the subsequent observable nonlinear response difference. Second, based on the intensity of the two pulses, the time interval between them is determined to ensure that the first echo waveform and the second echo waveform do not overlap or interfere with each other in time, thereby ensuring the accuracy of subsequent feature extraction.

[0026] Step S2: Based on the dual-pulse emission parameters, emit a first laser pulse and a second laser pulse in each scanning direction, and acquire the first echo waveform corresponding to the first laser pulse and the second echo waveform corresponding to the second laser pulse.

[0027] It should be noted that after parameter settings are completed, the lidar scans according to the preset dual-pulse emission parameters. For each scanning direction, the system sequentially emits a first laser pulse and a second laser pulse, which are closely connected in time but have different intensities. After emission, the receiver continuously acquires echo signals at a high sampling rate, recording the first echo waveform corresponding to the first laser pulse and the second echo waveform corresponding to the second laser pulse. This method of actively emitting pulses of different intensities and completely acquiring echo waveforms provides the original data foundation for the subsequent extraction of multi-dimensional time-domain features in this invention.

[0028] Step S3: Generate a three-dimensional point cloud based on the first echo waveform and the second echo waveform, and extract at least several time-domain features from the first echo waveform and the second echo waveform, including echo intensity, pulse broadening degree, and energy distribution concentration. Associate the extracted time-domain features as feature vectors with the corresponding points in the three-dimensional point cloud.

[0029] It should be noted that after acquiring the echo waveforms, the system first calculates the distance of each reflection point based on the time information in the first and second echo waveforms, thereby generating a 3D point cloud containing three-dimensional coordinates. Simultaneously, the system extracts multiple temporal features from each echo waveform, including echo intensity (reflecting the strength of the reflected energy), pulse broadening (reflecting the stretching of the echo waveform along the time axis, related to the complexity of the scattering path of the laser), and energy distribution concentration (reflecting whether the echo energy is concentrated at one time point or dispersed across multiple time points). These temporal features are combined into feature vectors and associated with corresponding points in the 3D point cloud, ensuring that each point in the point cloud not only contains spatial coordinate information but also carries a physical fingerprint describing its echo characteristics.

[0030] Step S4: Based on the spatial distribution of the 3D point cloud and the temporal characteristics of each point in the 3D point cloud, identify suspected obstacle targets; based on the temporal characteristics of the first echo waveform and the second echo waveform of the corresponding point of the suspected obstacle target, calculate the intensity ratio deviation, the width ratio deviation, and the energy distribution deviation, and weight and fuse the three to obtain a comprehensive discrimination score; when the comprehensive discrimination score exceeds the first threshold, combine spatial geometric consistency verification and temporal stability verification to make a comprehensive judgment to confirm that the suspected obstacle target is a multipath artifact or a real obstacle.

[0031] It should be noted that, based on the spatial distribution of the 3D point cloud and the temporal characteristics associated with each point, the system identifies target areas that may contain obstacles, referred to as suspected obstacle targets. For each suspected obstacle target, the system calculates three deviations using the temporal characteristics of its corresponding point: intensity ratio deviation (measuring the difference between the actual echo intensity ratio and the theoretical emission intensity ratio), broadening ratio deviation (measuring the difference in the broadening degree of two pulse echoes), and energy distribution deviation (measuring the difference in the energy concentration of two pulses). These three deviations are weighted and fused according to preset weights to obtain a comprehensive discrimination score. When this score exceeds the threshold corresponding to the current dust concentration, the system further initiates spatial geometric consistency verification and temporal stability verification: spatial geometric consistency verification checks whether there is a highly reflective surface that makes the zigzag optical path consistent with the measured distance to confirm whether the target is an artifact caused by multipath reflection; temporal stability verification tracks the position change of the target in multiple frames of point cloud to determine whether it has the smooth motion trajectory that a real object should have. Combining the results of the above multiple verifications, the system finally confirms whether the suspected obstacle target is a real obstacle or a multipath artifact.

[0032] It is worth mentioning that the first threshold corresponding to the current dust concentration is a dynamically set judgment benchmark value based on the dust concentration estimated by the pre-scan: the higher the dust concentration, the lower the threshold is set, so that the system is more sensitive to the recognition of multipath artifacts; conversely, the lower the dust concentration, the higher the first threshold is set, so as to avoid misjudging real obstacles as artifacts. Through this adaptive adjustment, the system can maintain optimal discrimination performance in different dust environments.

[0033] In this specific embodiment, in step S4, The intensity ratio deviation is the value obtained by dividing the absolute value of the difference between the actual ratio of the echo intensity extracted from the first echo waveform and the echo intensity extracted from the second echo waveform at the point corresponding to the suspected obstacle target by the emission intensity ratio. The pulse width ratio deviation is the absolute value of the difference between 1 and the ratio of the pulse width ratio extracted from the first echo waveform to the pulse width ratio extracted from the second echo waveform at the point corresponding to the suspected obstacle target. The energy distribution deviation is the absolute value of the difference between the energy distribution concentration extracted from the first echo waveform and the energy distribution concentration extracted from the second echo waveform at the point corresponding to the suspected obstacle target.

[0034] It should be noted that in step S4, the specific calculation methods for the three deviations are as follows: Intensity ratio deviation refers to the absolute value of the difference between the actual ratio of the echo intensity of the first echo waveform to the echo intensity of the second echo waveform at the point corresponding to the suspected obstacle target, and the ratio of the emission intensity of the first laser pulse to the emission intensity of the second laser pulse, divided by the emission intensity ratio. This deviation reflects the degree of deviation between the actual echo intensity ratio and the theoretical linear ratio. Broadening ratio deviation refers to the absolute value of the difference between the ratio of the pulse broadening of the first echo waveform to the pulse broadening of the second echo waveform and 1. This deviation reflects the broadening change caused by the difference in scattering paths between the two pulses. Energy distribution deviation refers to the absolute value of the difference between the energy distribution concentration of the first echo waveform and the energy distribution concentration of the second echo waveform. This deviation reflects the difference in the degree of energy dispersion between the two pulses. These three deviations characterize the nonlinear response features of multipath artifacts from different physical dimensions.

[0035] In this specific embodiment, the spatial geometric consistency verification in step S4 includes: Points with echo intensity higher than a preset intensity threshold are extracted from the 3D point cloud, and the extracted points are clustered into high reflectivity surface candidates. For a suspected obstacle target, traverse each high reflectivity surface candidate and determine whether there is a high reflectivity surface such that the zigzag optical path from the lidar through the high reflectivity surface to the suspected obstacle target is consistent with the lidar's measured distance and satisfies the law of reflection. If it exists, the high reflectivity surface is identified as a multipath reflectivity surface of the suspected obstacle target, and the confidence that the suspected obstacle target is a multipath artifact is increased; if it does not exist, the confidence that the suspected obstacle target is a multipath artifact is decreased.

[0036] It should be noted that the specific implementation of the spatial geometric consistency verification in step S4 is as follows: First, points with echo intensity higher than a preset intensity threshold are selected from the 3D point cloud. These points typically correspond to high reflectivity surfaces (such as metal equipment, waterlogged roads, etc.), and these points are clustered into high reflectivity surface candidates. Then, for each suspected obstacle target, each high reflectivity surface in these high reflectivity surface candidates is traversed to determine whether there exists such a high reflectivity surface such that the total length of the broken-line path from the lidar, after reflection through the high reflectivity surface, to the suspected obstacle target is consistent with the distance value of the target directly measured by the lidar, and the incident angle at the reflection point is equal to the reflection angle. If such a high reflectivity surface exists, it is confirmed as the multipath reflectivity surface of the suspected obstacle target, and the confidence that the target is a multipath artifact is increased; if it does not exist, the confidence is decreased.

[0037] In this specific embodiment, the timing stability verification in step S4 includes: Multi-frame trajectory tracking is established for suspected obstacle targets, and their position drift amplitude is calculated. If the drift amplitude exceeds the preset position drift threshold, it is determined to be a multipath artifact; otherwise, it is determined to be a real obstacle. The position drift threshold is calibrated according to the motion characteristics of the work platform.

[0038] It should be noted that the temporal stability verification in step S4 is implemented as follows: For each suspected obstacle target, the system tracks its position in multiple consecutive frames of point cloud and establishes a trajectory record. The motion stability is determined by calculating the target's position drift amplitude (i.e., the variance or offset of the position coordinates) across multiple frames. Real obstacles, being physical entities, should exhibit kinematic changes, presenting a smooth and continuous trajectory; while multipath artifacts, affected by factors such as dust drift and changes in the angle of highly reflective surfaces, often exhibit irregular jitter or random jumps in position. Therefore, if the position drift amplitude exceeds a preset position drift threshold, it is determined to be a multipath artifact; otherwise, it is determined to be a real obstacle. This position drift threshold can be calibrated based on the normal motion characteristics of the work platform (such as mining vehicles, construction machinery, etc.) to ensure the accuracy of the judgment.

[0039] Step S5: If the suspected obstacle is determined to be a multipath artifact, the target is filtered out from the 3D point cloud and artifact identification information is sent to the remote operation terminal; if the suspected obstacle is determined to be a real obstacle, the target information is retained and obstacle avoidance command or alarm information is generated based on its position and motion state for control.

[0040] It should be noted that, based on the determination result of step S4, the system executes the corresponding remote control strategy. If it is determined to be a multipath artifact, the system filters the target out of the 3D point cloud to prevent it from interfering with subsequent decision-making and control. Simultaneously, it sends artifact identification information to the remote operator, enabling the operator to understand the currently perceived false target situation and avoid triggering unnecessary obstacle avoidance actions. If it is determined to be a real obstacle, the system retains the target information and generates corresponding obstacle avoidance commands or alarm information based on its position and movement status, sending them to the remote control terminal or vehicle-mounted actuator to ensure that the work platform can promptly avoid real risks and guarantee operational safety.

[0041] Through the coordinated operation of the above five steps, this invention achieves accurate identification and filtering of multipath artifacts in dusty environments, significantly improving the reliability and security of lidar-based remote control systems.

[0042] This invention, through actively transmitting dual-pulse sequences with different intensities and extracting multiple time-domain features such as echo intensity ratio, pulse broadening, and energy distribution concentration for weighted fusion, fundamentally stimulates the physical differences in nonlinear responses between real obstacles and multipath artifacts. This solves the problem that existing technologies cannot distinguish between the two by relying solely on spatial geometric information, and significantly improves the accuracy of target recognition.

[0043] This invention introduces spatial geometric consistency verification and temporal stability verification as a dual verification mechanism. By utilizing the geometric constraint that multipath artifacts must pass through a high-reflectivity surface and their unstable temporal characteristics, the preliminary judgment results are cross-verified, which effectively reduces the false judgment rate and avoids the risk of frequent false stops caused by multipath artifacts or collisions caused by missed detection of real obstacles.

[0044] The embodiments of this invention are entirely based on the characteristics of lidar itself, without the need to add additional hardware such as millimeter-wave radar or polarization cameras. They can be deployed through software upgrades, which improves the reliability of perception while controlling system costs, and has good economic efficiency and feasibility.

[0045] The embodiments of the present invention dynamically adjust the dual-pulse emission parameters and discrimination threshold according to the dust concentration, so that the system can adapt to dust environments with different concentrations, maintain efficient detection under light dust conditions, enhance the ability to distinguish between genuine and counterfeit products under heavy dust conditions, and ensure the stability and reliability of remote control in complex and harsh environments.

[0046] In summary, this invention utilizes active double-pulse coding to elicit the nonlinear response differences between real targets and multipath artifacts. Combined with multi-dimensional temporal feature fusion, spatial geometric consistency verification, and temporal stability verification, it achieves accurate identification and filtering of multipath artifacts. This method requires no additional hardware and can adaptively adjust detection parameters based on dust concentration. It effectively solves the technical challenge of distinguishing between real obstacles and false targets in dusty environments, significantly improving the perception reliability and operational safety of remote control systems. It is suitable for unmanned remote control in high-dust environments such as mines and tunnels.

[0047] Based on the aforementioned lidar-based remote control method, this invention also provides a lidar-based remote control system, such as... Figure 2 As shown, the system includes: a parameter acquisition module 201, a scanning module 202, a 3D point cloud creation module 203, an obstacle judgment module 204, and a control module 205; The parameter acquisition module 201 is used to obtain the current dust concentration at the work site through pre-scanning of the lidar; and to obtain the dual-pulse emission parameters based on the current dust concentration; wherein, the dual-pulse emission parameters include the intensity and pulse interval of the first laser pulse and the second laser pulse, and the intensity of the first laser pulse and the intensity of the second laser pulse are different. The scanning module 202 is used to emit a first laser pulse and a second laser pulse in each scanning direction based on the dual-pulse emission parameters, and to acquire the first echo waveform corresponding to the first laser pulse and the second echo waveform corresponding to the second laser pulse. The three-dimensional point cloud building module 203 is used to generate a three-dimensional point cloud based on the first echo waveform and the second echo waveform, and extract multiple time-domain features from the first echo waveform and the second echo waveform, including at least echo intensity, pulse broadening degree and energy distribution concentration, and associate the extracted time-domain features as feature vectors with the corresponding points in the three-dimensional point cloud. The obstacle judgment module 204 is used to identify suspected obstacle targets based on the spatial distribution of the 3D point cloud and the temporal characteristics of each point in the 3D point cloud; based on the temporal characteristics of the first echo waveform and the second echo waveform of the corresponding point of the suspected obstacle target, it calculates the intensity ratio deviation, the width ratio deviation, and the energy distribution deviation, and then weights and fuses the three to obtain a comprehensive judgment score; when the comprehensive judgment score exceeds a first threshold, it combines spatial geometric consistency verification and temporal stability verification to make a comprehensive judgment to confirm whether the suspected obstacle target is a multipath artifact or a real obstacle. The control module 205 is used to filter out the suspected obstacle from the 3D point cloud if it is determined to be a multipath artifact, and send artifact identification information to the remote operation terminal; if the suspected obstacle is determined to be a real obstacle, the target information is retained, and obstacle avoidance instructions or alarm information are generated based on its position and motion state for control.

[0048] Optionally, the parameter acquisition module 201 is specifically used for: Based on the current dust concentration, the intensities of the first and second laser pulses are obtained; the higher the current dust concentration, the greater the difference in intensity between the first and second laser pulses. The pulse interval between the first laser pulse and the second laser pulse is determined based on their respective intensities to ensure that the first echo waveform and the second echo waveform do not interfere with each other.

[0049] Optionally, the intensity ratio deviation is the value obtained by dividing the absolute value of the difference between the actual ratio of the echo intensity extracted from the first echo waveform and the echo intensity extracted from the second echo waveform at the point corresponding to the suspected obstacle target by the emission intensity ratio. The pulse width ratio deviation is the absolute value of the difference between 1 and the ratio of the pulse width ratio extracted from the first echo waveform to the pulse width ratio extracted from the second echo waveform at the point corresponding to the suspected obstacle target. The energy distribution deviation is the absolute value of the difference between the energy distribution concentration extracted from the first echo waveform and the energy distribution concentration extracted from the second echo waveform at the point corresponding to the suspected obstacle target.

[0050] Optionally, the obstacle determination module 204 performs spatial geometric consistency verification, specifically including: Points with echo intensity higher than a preset intensity threshold are extracted from the 3D point cloud, and the extracted points are clustered into high reflectivity surface candidates. For a suspected obstacle target, traverse each high reflectivity surface candidate and determine whether there is a high reflectivity surface such that the zigzag optical path from the lidar through the high reflectivity surface to the suspected obstacle target is consistent with the lidar's measured distance and satisfies the law of reflection. If it exists, the high reflectivity surface is identified as a multipath reflectivity surface of the suspected obstacle target, and the confidence that the suspected obstacle target is a multipath artifact is increased; if it does not exist, the confidence that the suspected obstacle target is a multipath artifact is decreased.

[0051] Optionally, the timing stability verification of the obstacle judgment module 204 includes the following steps: Multi-frame trajectory tracking is established for suspected obstacle targets, and their position drift amplitude is calculated. If the drift amplitude exceeds the preset position drift threshold, it is determined to be a multipath artifact; otherwise, it is determined to be a real obstacle. The position drift threshold is calibrated according to the motion characteristics of the work platform.

[0052] This invention utilizes active double-pulse coding to elicit the nonlinear response differences between real targets and multipath artifacts. Combined with multi-dimensional temporal feature fusion, spatial geometric consistency verification, and temporal stability verification, it achieves accurate identification and filtering of multipath artifacts. This method requires no additional hardware and can adaptively adjust detection parameters based on dust concentration. It effectively solves the technical challenge of distinguishing between real obstacles and false targets in dusty environments, significantly improving the perception reliability and operational safety of remote control systems. It is suitable for unmanned remote control in high-dust environments such as mines and tunnels.

[0053] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0054] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0055] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A remote control method based on lidar, characterized in that, The method includes: Step S1: Obtain the current dust concentration at the work site through pre-scanning of the lidar; obtain the dual-pulse emission parameters based on the current dust concentration; wherein, the dual-pulse emission parameters include the intensity and pulse interval of the first laser pulse and the second laser pulse, and the intensity of the first laser pulse and the intensity of the second laser pulse are different; Step S2: Based on the dual-pulse emission parameters, emit the first laser pulse and the second laser pulse in each scanning direction, and acquire the first echo waveform corresponding to the first laser pulse and the second echo waveform corresponding to the second laser pulse; Step S3: Generate a three-dimensional point cloud based on the first echo waveform and the second echo waveform, and extract at least several time-domain features from the first echo waveform and the second echo waveform, including echo intensity, pulse broadening degree, and energy distribution concentration, and associate the extracted time-domain features as feature vectors with the corresponding points in the three-dimensional point cloud; Step S4: Based on the spatial distribution of the 3D point cloud and the temporal characteristics of each point in the 3D point cloud, identify suspected obstacle targets; based on the temporal characteristics of the first echo waveform and the second echo waveform of the corresponding point of the suspected obstacle target, calculate the intensity ratio deviation, the broadening ratio deviation, and the energy distribution deviation, and weight and fuse the three to obtain a comprehensive discrimination score; when the comprehensive discrimination score exceeds a first threshold, combine spatial geometric consistency verification and temporal stability verification to make a comprehensive judgment and confirm that the suspected obstacle target is a multipath artifact or a real obstacle; Step S5: If the suspected obstacle target is determined to be a multipath artifact, the target is filtered out from the 3D point cloud and artifact identification information is sent to the remote operation terminal; if the suspected obstacle target is determined to be a real obstacle, the target information is retained, and obstacle avoidance commands or alarm information are generated based on its position and motion state for control.

2. The remote control method based on lidar according to claim 1, characterized in that, The step S1 of obtaining the dual-pulse emission parameters based on the current dust concentration includes: Based on the current dust concentration, the intensities of the first laser pulse and the second laser pulse are obtained; wherein, the higher the current dust concentration, the greater the difference in intensity between the first laser pulse and the second laser pulse; Based on the respective intensities of the first laser pulse and the second laser pulse, the pulse interval of the first laser pulse and the second laser pulse is determined to ensure that the first echo waveform and the second echo waveform do not interfere with each other.

3. The remote control method based on lidar according to claim 1, characterized in that, In step S4, The intensity ratio deviation is the actual ratio of the echo intensity extracted from the first echo waveform and the echo intensity extracted from the second echo waveform at the point corresponding to the suspected obstacle target, divided by the absolute value of the difference between the emission intensity ratio of the first laser pulse and the second laser pulse, and the emission intensity ratio. The deviation of the pulse width ratio is the absolute value of the difference between 1 and the ratio of the pulse width ratio extracted from the first echo waveform at the point corresponding to the suspected obstacle target. The energy distribution deviation is the absolute value of the difference between the energy distribution concentration extracted from the first echo waveform and the energy distribution concentration extracted from the second echo waveform at the point corresponding to the suspected obstacle target.

4. The remote control method based on lidar according to claim 1, characterized in that, The spatial geometric consistency verification in step S4 includes: Points with echo intensity higher than a preset intensity threshold are extracted from the three-dimensional point cloud, and the extracted points are clustered into high reflectivity surface candidates. For the suspected obstacle target, traverse each high reflectivity surface in the candidate high reflectivity surfaces and determine whether there is a high reflectivity surface such that the zigzag optical path from the lidar through the high reflectivity surface to the suspected obstacle target is consistent with the actual distance measured by the lidar and satisfies the law of reflection. If it exists, the high reflectivity surface is identified as the multipath reflectivity surface of the suspected obstacle target, and the confidence that the suspected obstacle target is a multipath artifact is increased; if it does not exist, the confidence that the suspected obstacle target is a multipath artifact is decreased.

5. The remote control method based on lidar according to claim 1, characterized in that, The timing stability verification in step S4 includes: Multi-frame trajectory tracking is established for the suspected obstacle target, and its position drift amplitude is calculated. If the drift amplitude exceeds the preset position drift threshold, it is determined to be a multipath artifact; otherwise, it is determined to be a real obstacle. The position drift threshold is calibrated according to the motion characteristics of the working platform.

6. A remote control system based on lidar, characterized in that, The system includes: a parameter acquisition module, a scanning module, a 3D point cloud creation module, an obstacle judgment module, and a control module; The parameter acquisition module is used to obtain the current dust concentration at the work site through pre-scanning of the lidar; and to obtain dual-pulse emission parameters based on the current dust concentration; wherein the dual-pulse emission parameters include the intensity and pulse interval of the first laser pulse and the second laser pulse, and the intensity of the first laser pulse and the intensity of the second laser pulse are different; The scanning module is used to emit the first laser pulse and the second laser pulse in each scanning direction based on the dual-pulse emission parameters, and to acquire the first echo waveform corresponding to the first laser pulse and the second echo waveform corresponding to the second laser pulse. The three-dimensional point cloud building module is used to generate a three-dimensional point cloud based on the first echo waveform and the second echo waveform, and extract multiple time-domain features from the first echo waveform and the second echo waveform, including at least echo intensity, pulse broadening degree, and energy distribution concentration, and associate the extracted time-domain features as feature vectors with the corresponding points in the three-dimensional point cloud. The obstacle detection module is used to identify suspected obstacle targets based on the spatial distribution of the three-dimensional point cloud and the temporal characteristics of each point in the three-dimensional point cloud; based on the temporal characteristics of the first echo waveform and the second echo waveform of the corresponding point of the suspected obstacle target, it calculates the intensity ratio deviation, the broadening ratio deviation, and the energy distribution deviation, and weights and fuses the three to obtain a comprehensive discrimination score; when the comprehensive discrimination score exceeds a first threshold, it combines spatial geometric consistency verification and temporal stability verification to make a comprehensive judgment to confirm that the suspected obstacle target is a multipath artifact or a real obstacle; The control module is used to filter out the suspected obstacle from the 3D point cloud and send the artifact identification information to the remote operation terminal if the suspected obstacle is determined to be a multipath artifact; if the suspected obstacle is determined to be a real obstacle, the target information is retained and obstacle avoidance instructions or alarm information are generated based on its position and motion state for control.

7. The remote control system based on lidar according to claim 6, characterized in that, The parameter acquisition module is specifically used for: Based on the current dust concentration, the intensities of the first laser pulse and the second laser pulse are obtained; wherein, the higher the current dust concentration, the greater the difference in intensity between the first laser pulse and the second laser pulse; Based on the respective intensities of the first laser pulse and the second laser pulse, the pulse interval of the first laser pulse and the second laser pulse is determined to ensure that the first echo waveform and the second echo waveform do not interfere with each other.

8. The remote control system based on lidar according to claim 6, characterized in that, The intensity ratio deviation is the actual ratio of the echo intensity extracted from the first echo waveform and the echo intensity extracted from the second echo waveform at the point corresponding to the suspected obstacle target, divided by the absolute value of the difference between the emission intensity ratio of the first laser pulse and the second laser pulse, and the emission intensity ratio. The deviation of the pulse width ratio is the absolute value of the difference between 1 and the ratio of the pulse width ratio extracted from the first echo waveform at the point corresponding to the suspected obstacle target. The energy distribution deviation is the absolute value of the difference between the energy distribution concentration extracted from the first echo waveform and the energy distribution concentration extracted from the second echo waveform at the point corresponding to the suspected obstacle target.

9. The remote control system based on lidar according to claim 6, characterized in that, The spatial geometric consistency verification of the obstacle judgment module specifically includes: Points with echo intensity higher than a preset intensity threshold are extracted from the three-dimensional point cloud, and the extracted points are clustered into high reflectivity surface candidates. For the suspected obstacle target, traverse each high reflectivity surface in the candidate high reflectivity surfaces and determine whether there is a high reflectivity surface such that the zigzag optical path from the lidar through the high reflectivity surface to the suspected obstacle target is consistent with the actual distance measured by the lidar and satisfies the law of reflection. If it exists, the high reflectivity surface is identified as the multipath reflectivity surface of the suspected obstacle target, and the confidence that the suspected obstacle target is a multipath artifact is increased; if it does not exist, the confidence that the suspected obstacle target is a multipath artifact is decreased.

10. The remote control system based on lidar according to claim 6, characterized in that, The timing stability verification of the obstacle determination module specifically includes: Multi-frame trajectory tracking is established for the suspected obstacle target, and its position drift amplitude is calculated. If the drift amplitude exceeds the preset position drift threshold, it is determined to be a multipath artifact; otherwise, it is determined to be a real obstacle. The position drift threshold is calibrated according to the motion characteristics of the working platform.

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