652 results about "Loopback" patented technology

The invention relates to a dynamic environment semantic SLAM method based on monocular vision and LiDAR fusion, and belongs to the technical field of robots. The method comprises the following steps: 1) performing instance segmentation on point clouds to obtain unknown motion state point clouds, pure static road sign point clouds and ground attribute point clouds, identifying semi-static and dynamic road sign point clouds through initial pose estimation and Kalman filtering, and then performing accurate pose estimation; 2) segmenting and enhancing the image instance, extracting SuperPoint feature points, projecting a dynamic road sign to the segmented image so as to distinguish static and non-static points, tracking static features by adopting a plurality of tracking strategies, and optimizing the pose by utilizing a local map; 3) carrying out heterogeneous sensor attitude fusion; 4) outputting an optimized track by combining two loopback detection results; and 5) constructing a global pure static semantic map. According to the method, the dynamic environment influence is considered, and the positioning precision and the mapping quality of the system in the dynamic complex environment are improved.

The invention discloses an ultra-wideband laser radar inertial navigation cooperative SLAM (Simultaneous Localization and Mapping) method and system, which are suitable for robot positioning and mapping tasks in a GPS (Global Positioning System)-free environment. According to the method, high-frequency motion priori of an IMU, relative pose constraint of LiDAR and absolute ranging information of UWB are fused, a unified factor graph optimization model is constructed, and multi-sensor cooperative positioning is realized; in the system initialization stage, IMU bias calibration, UWB base station coordinate configuration and LiDAR initial attitude estimation are completed; in the operation process, IMU pre-integration is utilized to predict the pose, LiDAR point cloud registration is utilized to obtain the relative motion between key frames, UWB ranging is combined to construct a residual item, a self-adaptive weight mechanism is introduced to suppress ranging abnormity, and the fusion robustness is improved; the system supports geometric feature loopback detection, cross-frame constraints are constructed in combination with UWB to carry out closed-loop optimization, and an optimization result is used for real-time incremental updating of a global map; the method has the advantages of high positioning precision, strong anti-interference capability, wide application scene and the like.

The invention relates to the field of virtual reality positioning, and discloses a VR large-space positioning interaction system based on multi-modal perception, and the system comprises the steps: deploying a multi-modal sensor to obtain sensing data, carrying out the visual feature extraction and preprocessing, building a sparse point cloud map in a matching manner, carrying out the scale calibration, and constructing an environment model; pre-judging a UWB signal path based on an environment model, performing error optimization compensation on an NLOS state, and performing observation updating and fusion through degradation detection to obtain a predicted state change; constructing an interactive perception network, and tracking the hands and the whole body; tactile feedback is realized by using a layered tactile system, and a tactile effect is generated by using vibration frequency mapping; the transmission efficiency is improved by using a beam forming technology, an edge cloud server cluster renders a virtual scene, and the scene is pre-rendered in advance to offset network and rendering delay; an online calibration mechanism is designed, and system errors are corrected through visual loopback detection, UWB beacon dynamic correction and IMU drift compensation.

The invention belongs to the technical field of robotics and computer vision, and relates to a multi-source information fusion outdoor inspection scene three-dimensional color geometric model reconstruction method, which comprises the following steps: (1) inertial measurement unit IMU internal reference calibration initialization, (2) laser radar and inertial measurement unit IMU combined external reference calibration, (3) state estimation, (4) using an i-Octree structure to store an updated map, and (5) performing multi-source information fusion. (5) carrying out loopback detection to solve the mileage drift problem; (6) carrying out data fusion of the laser radar and the color camera; and (7) carrying out 3D global positioning. The method has the advantages that the information of the laser radar and the information of the color camera are fused, the one-to-one mapping relation between each space point in the three-dimensional point cloud and a certain pixel point in the color image is established, visual information is more completely described, and the visual information is more accurately and accurately positioned by using a 3D global positioning method. And high-precision reconstruction of the three-dimensional color geometric model is realized.

The invention discloses a multi-sensor fusion SLAM (Simultaneous Localization and Mapping) method in a dynamic scene, which belongs to the field of robot perception and comprises the following steps of: preprocessing visual, laser and IMU (Inertial Measurement Unit) data, including time synchronization and space synchronization; a sensor mathematical model is established, a visual inertia subsystem and a laser inertia subsystem are constructed at the same time, and the two systems are fused in a tight coupling mode. The laser inertial subsystem assists the visual inertial subsystem to complete scale recovery, and the visual inertial subsystem assists the laser inertial subsystem to carry out loopback detection. And finally, establishing a global nonlinear optimized factor graph model in a graph optimization mode, and adding a laser odometer factor, an IMU pre-integration factor, a visual factor and a loopback detection factor into a factor graph to obtain global optimal pose estimation and a three-dimensional map. In a dynamic environment, information of a plurality of sensors is fused, so that the influence of a dynamic object on positioning is reduced, and higher positioning precision and robustness are achieved.

The invention relates to the technical field of laser SLAM, and particularly discloses a laser SLAM method based on ground segmentation, and the method comprises the steps: obtaining laser radar point cloud data information, carrying out the point cloud preprocessing of the laser radar point cloud data, and obtaining ground point cloud features and non-ground point cloud features; respectively carrying out feature extraction, feature matching and pose estimation on the ground point cloud features and the non-ground point cloud features to obtain a pose nonlinear optimization constraint result; selecting a key frame according to a pose nonlinear optimization constraint result, constructing a local map according to pose information of the key frame, and obtaining a local map construction result; loopback detection is carried out according to the local map construction result, and a loopback detection constraint result is obtained; and performing global pose optimization processing according to a loopback detection constraint result and a local map construction result to obtain a global pose and global map construction result. The laser SLAM method based on ground segmentation provided by the invention can improve the positioning precision and the operation stability.

The invention relates to a dynamic scene vision SLAM method and system based on point-line complementation, and belongs to the field of simultaneous mapping and positioning of mobile robots. According to the method, target prior information monitoring is carried out on an image through a semantic module, and dynamic feature points inconsistent with camera motion are identified; visual feature extraction is carried out on the image through a tracking module, and the visual feature extraction comprises point feature extraction and line feature extraction; removing the point features preliminarily extracted by the tracking module according to the dynamic feature points identified by the semantic module, fusing the obtained static point features and line features, expanding the static point features according to the fused line feature constraints, and screening out key frames according to the point and line features; and finally, completing map construction and optimization updating through a local mapping module and a loopback detection and global BA optimization module. According to the method, mapping limitation and deviation possibly caused by single scene texture information can be overcome, and the real-time performance, the mapping precision and the robustness of mapping can be improved.

The invention discloses a laser SLAM mapping positioning method and system in a dynamic environment. The method comprises the steps that point cloud information and IMU measurement data in the dynamic environment are collected; the data preprocessing module carries out pre-integration by using IMU data so as to remove point cloud distortion; the pose estimation module realizes positioning based on error Kalman filtering by using IMU measurement information and point cloud feature matching; the dynamic point filtering module stores the three-dimensional point cloud in a two-dimensional image mode, judges whether the three-dimensional point cloud is a dynamic point or not based on the change condition of the point cloud at the same pixel, and finally carries out clustering elimination to output the point cloud only containing a static scene; and the loopback detection module extracts descriptors based on the static scene point cloud, and corrects the pose deviation through ICP (Inductively Coupled Plasma) matching. According to the method, the problem that dynamic object interference occurs in the mapping positioning scene is effectively solved, the dynamic points do not interfere with positioning, the map does not contain ghosts generated by moving objects, help is provided for follow-up navigation and the like, and the robustness and accuracy of mapping positioning are improved.

The invention discloses a distributed multi-robot autonomous collaborative exploration and mapping method. Comprising the steps that in the exploration process, each robot explores and obtains a global subspace state map, if each robot does not communicate with other robots, the current robot conducts global exploration path planning and local exploration path planning according to the global subspace state map explored by the current robot, and if not, the current robot conducts local exploration path planning; furthermore, autonomous local exploration and global exploration are carried out; if each robot communicates with other robots, the current robot performs global exploration path planning according to the latest global subspace state map and performs local exploration path planning combined with loopback constraint, and further performs collaborative local exploration and global exploration; and after exploration is completed, distributed collaborative mapping is carried out, and a positioning mapping result is obtained. According to the method, autonomy and accuracy of multi-machine cooperative sensing work are improved, and the method has good guidance value for application of multi-machine cooperation in a real world scene.

The invention relates to a loopback detection method based on active vision and text semantics. The loopback detection method comprises the steps that the pose of a mechanical arm is changed to drive a camera to conduct image collection on the surrounding environment to obtain a front view text image; synchronously controlling the laser radar to collect multiple frames of point clouds to screen key frame point clouds; selecting a key text image corresponding to the key frame point cloud from the multiple frames of front view text images, extracting text information in the key text image, and converting the text information into a global coordinate system; calculating the similarity between the text information in the key text image under the global coordinate system and the historical text information obtained at the historical moment, and determining candidate loopback point clouds according to the similarity; and performing geometric consistency calculation on the candidate loopback point clouds to obtain a distance value so as to obtain a loopback detection result. The technical problem of loopback detection errors caused by feature degradation, sparse or similar environments is solved by actively and visually collecting the front view image of the text information and combining the double loopback verification of the point cloud, and the precision and robustness of system positioning and mapping are improved.

The invention belongs to the field of robot and computer vision, and particularly relates to a visual SLAM (Simultaneous Localization and Mapping) back-end optimization technology based on a data self-adaptive iterative reweighted robust kernel. Comprising the following steps: S1, a front-end data reading and processing module: a robot carries a camera to collect an image, performs feature extraction and feature matching on the image, and generates an initial camera pose and map points; s2, a rear-end optimization module: according to the initial camera pose and map point generated at the front end, calculating a median absolute deviation in an adaptive strategy, calculating a robustness parameter, then substituting the median absolute deviation and the robustness parameter into a target function, simplifying the target function by using an iterative reweighting strategy, and finally solving the optimal camera pose and map point by using an L-M algorithm; s3, the loopback detection module detects loopback according to whether the robot reaches a previous position or not, and transmits information to a rear end for processing; and S4, constructing a map module: constructing an environment map according to the optimized camera pose and map points.

The invention provides an SLAM method based on end-to-end visual odometer and aerial view multi-sensor fusion, and belongs to the technical field of automatic navigation and mobile robots. According to the method, a visual odometer factor is designed by using a lightweight improved LoFTR network, so that the calculation amount is reduced, and the positioning and mapping precision and robustness of a visual robot in a structure-free and weak-texture environment are improved. A multi-sensor dynamic fusion model based on an aerial view is designed and constructed, and an IMU factor, a visual odometer factor, a laser odometer factor and a loopback detection factor are integrated. By introducing a self-adaptive weighting mechanism, weighted splicing is carried out on features from different sensors so as to realize optimal combination of feature information. According to the algorithm, the calculation amount is reduced, the calculation efficiency is improved, and the SLAM system can realize real-time or near-real-time positioning and mapping. In addition, the adaptive weighting strategy can be dynamically adjusted according to the actual environment and the sensor state, and the adaptability and flexibility of the algorithm in different scenes are improved.

A system may include a first DAS station, comprising a first plurality of interrogator units to launch a first plurality of DAS signals in a first direction, wherein a first interrogator unit is configured to launch a first DAS signal at a first interrogation rate and first wavelength, wherein a second interrogator unit is configured to launch a second DAS signal at a second wavelength and second interrogation rate, less than the first interrogation rate. The system may include a loopback array, comprising a plurality of loopbacks, arranged over a plurality of spans, wherein a first set of proximate loopbacks nearest to the first DAS station are configured to route back the first DAS signal to the first DAS station, and wherein a first set of intermediate loopbacks, located further from the first DAS station, are configured to route back the second DAS signal to the first DAS station.

The invention discloses a three-dimensional point cloud map construction method, system and device and a medium. The method comprises the following steps: acquiring a color image, a depth image and inertial measurement unit data; constructing an image pyramid according to the color image, and extracting feature points; performing dynamic target detection and feature point screening on the image pyramid through target detection model detection; according to the feature points and inertial measurement unit data, performing preliminary estimation and refined optimization on the pose; performing thread tracking and key frame judgment according to the distribution and poses of the feature points to obtain a key frame, and updating a local map; carrying out loopback detection on the current frame, and optimizing a global map; and performing point cloud generation, point cloud splicing and point cloud optimization according to the color image and the depth image corresponding to the key frame to obtain a three-dimensional point cloud map. By screening the dynamic feature points and fusing the IMU data, high-precision indoor positioning and map modeling are realized, and the method can be widely applied to the technical field of intelligent positioning and mapping.

The invention discloses a pipe network detection robot positioning method, device and equipment, a medium and a product. Images are obtained through a binocular camera arranged on the detection robot, and a left eye image and a right eye image are transmitted in a topic mode; extracting feature points and descriptors of the left eye image and the right eye image by using an acceleration feature network; generating an initialized pipeline map by using an SLAM system and determining an initial pose of a camera through a tracking thread; the initial pose is optimized through the feature points and the descriptors; constructing a cylinder according to the initialized pipeline map, and inserting key frames meeting conditions into a local mapping thread; generating map points and cylindrical points by using the local mapping thread and the received key frame, and executing local BA; and a closed-loop detection thread is utilized to search for candidate loopback frames through an XFat database, similarity transformation is calculated to obtain an accumulated error, and a global BA optimization map is executed to position the detection robot. According to the scheme, precise positioning of the pipe network detection robot in a pipeline complex scene can be achieved.

The present disclosure provides example network devices and communication methods. One example method includes transmitting, by a first network device, according to a network protocol, a packet to a second network device. The packet includes loopback request information in a header of a layer of a network model. A loopback reply packet is received. The loopback reply packet includes, in a payload, at least a portion of the packet including at least one header of the packet. The loopback request information requests the second network device to transmit the loopback reply packet to the first network device.

The invention discloses a visual inertial positioning method for visible light and infrared cross-modal matching, and belongs to the technical field of multi-source fusion navigation. According to the invention, the problems of poor positioning effect and loopback detection failure in the prior art in a visual degradation environment are solved. According to the method, rich texture features of a visible light camera and features of an infrared camera which are not influenced by illumination conditions are combined, and a visual inertial positioning system with visible light and infrared cross-modal matching is constructed. Visual feature points and descriptors of a visible light image and a thermal infrared image are extracted through a public deep neural network, the confidence degree of the features is provided, classification management of cross-modal feature points is provided, then the depth of road sign points is recovered through two-view triangularization, loopback detection is carried out based on cosine similarity of bag-of-word vectors, and the visual feature points and descriptors of the visible light image and the thermal infrared image are obtained. And performing joint optimization by utilizing a depth estimation value, a loopback detection result and an inertia pre-integration measurement value to obtain positions and postures of the visible light camera and the infrared camera. The method can be applied to the field of multi-source fusion navigation.

The invention provides a visual inertial odometer method based on lightweight design, and relates to the technical field of automatic driving and robot navigation, efficient and accurate positioning and mapping can be realized on resource-limited equipment, and the method comprises the following steps: S1, inputting an RGB image and inertial measurement unit (IMU) data, and carrying out preprocessing to obtain an RGB image; a vision-inertia data stream with time alignment is obtained; s2, performing feature point detection and tracking on the visual image, and extracting key frame feature points to obtain visual feature information; s3, pre-integration is carried out on the IMU data, and relative motion information between adjacent key frames is calculated; s4, inputting the visual features and an IMU pre-integration result into a tight coupling frame, and estimating a pose, a speed and a sensor deviation through a sliding window optimization technology; s5, accumulated errors are corrected through a loopback detection technology, and the positioning and mapping precision is improved; and S6, outputting real-time pose, speed information and an environment three-dimensional map. According to the invention, vision and inertia information can be efficiently fused, the precision and robustness of the odometer are improved, and the system cost is reduced.

The invention relates to an audio SOC chip, an audio subsystem interface module thereof, and electronic equipment, and the audio subsystem interface module comprises a plurality of downlink path units which are used for carrying out the downlink processing of at least one path of digital audio data obtained from an application processor chip and / or a digital signal processing chip through a peripheral bus interface module, the audio data is output through the audio data transmission interface module; the plurality of uplink path units are used for performing uplink processing on at least one path of digital audio data input through the audio data transmission interface module and outputting the processed data to the application processor chip and / or the digital signal processing chip through the peripheral bus interface module; and the plurality of loopback path units are used for converging the at least one path of digital audio data input through the audio data transmission interface module and the digital audio data obtained by performing downlink processing through any downlink path unit in the plurality of downlink path units, and outputting the digital audio data through the audio data transmission interface module, so that the power consumption of a chip is reduced.

A probe card for a circuit probe test system and methods of fabrication thereof. The probe card includes a substrate portion, and a probe head including a guide plate located below the substrate portion and having a plurality of openings through the guide plate, and a conductive trace on the guide plate that extends between a pair of the openings. A plurality of probe pins extend through the openings through the guide plate, where a pair of probe pins are electrically connected by the conductive trace to form a loopback signal path. Accordingly, the loopback signals may be routed through the conductive trace located on and / or within the guide plate rather than through the substrate portion of the probe card. This may significantly reduce the total length of the loopback signal path, which may thereby improve the signal integrity (SI) during loopback testing of a device-under-test.

Enhanced security for Zero Trust networks is provided by SSH-customized tunnel clients / tunnel servers, a catalog service, and loopback address DNS mechanisms. Systems and methods provide Policy Enforcement Point (PEP) layer enhancements, strategically positioning the PEP between the user and the network resource. It manages network traffic flows and provides moderate control granularity, near-real-time enforcement decisions, low overheads, and broad applicability to TCP / IP traffic through modified tunneling implementations of Secure Shell (SSH). Unique use of SSH tunneling is utilized and adapted to selectively filter tunnel requests based on user entitlements, ensuring secure and authorized access to network resources. This method entails detailed assessment of tunneling requests, DNS manipulation, and the use of loopback address space for traffic redirection, all without requiring modifications to client-side applications. The approach significantly enhances network security by controlling access based on continuous verification of user entitlements, addressing the shortcomings of traditional network security models.

The invention provides a multi-sensor fusion positioning method and system based on factor graph optimization. The method comprises the following steps: acquiring measurement data of a current area acquired by multiple sensors; constructing pose factors according to the measurement data, wherein the pose factors comprise a pre-integration factor, an odometer factor, a loopback factor and a GPS factor; determining an optimized pose of the robot by adopting a factor graph optimization method according to the pose factors; and positioning the robot in the current area according to the optimized pose. According to the multi-sensor fusion positioning method and system based on factor graph optimization provided by the invention, the measurement data of the preorder state is utilized, and the pre-integration factor, the odometer factor, the loopback factor and the GPS factor are introduced, so that the accumulative error of the odometer is greatly reduced, and an effective means is provided for improving the precision and robustness of the system. According to the method, the real-time performance, robustness and accuracy of positioning are improved, and the method has great application research significance for autonomous navigation positioning of the robot.

The invention discloses a narrow environment high-precision laser inertial navigation and SLAM (Simultaneous Localization and Mapping) method based on adaptive parameters. According to the method, firstly, radar and IMU data are collected and preprocessed to form a unified input data stream; obtaining a pose prediction result by using the data stream, constructing an observation model, and obtaining a laser observation residual error; calculating a characteristic value proportion in the matching process of the point cloud and the sub-map, and judging a geometric degradation scene; when degradation is detected, adaptively adjusting a voxel filtering radius and a loopback detection threshold value; fusing the pose prediction result and the laser observation residual error by adopting a Kalman filtering model to obtain an optimal pose estimation result, and carrying out loopback detection and back-end map optimization under a dynamic threshold value to correct accumulated drift and update a voxel map; and finally outputting a continuous pose track and a three-dimensional voxel map. According to the method, the robustness and the positioning precision of the laser inertial navigation fusion SLAM in narrow scenes such as pipe galleries, tunnels and chemical plants are effectively improved.

A system for insitu AESA calibration includes a radar receiver / excitor (XR). The calibrated XR functions as a precision multi-channel relative amplitude and phase microwave frequency measurement device that executes a known AESA calibration methodology. An integrated RF sensor system-level built in test (BIT) is utilized for prognostic health monitoring and self-healing calibration. During calibration, quadrants of the AESA are iteratively calibrated via measurements by the XR. I / Q values are balanced for each quadrant relative to each other without the need for a separately calibrated measurement device for absolute calibration. A near field probe may be disposed for radiative loopback measurements. A T / R calibration circuit receives the loopback measurements and data from a Tx channel.

The invention belongs to the field of network bottleneck diagnosis, and discloses a host internal network delay diagnosis method based on a loopback test, which is characterized in that an RDMA network simulation environment is established by using NS-3 (Network Simulation 3) network simulation software based on a RoCE-v2 protocol, and an RDMA host internal network diagnosis tool is realized in the network. According to the tool, network bandwidth and delay in a host are used as measurement indexes, and the indexes are measured by carrying out loopback testing between RDMA NIC and PCIe endpoint in the host. After the measurement data is collected, the internal network condition of the host can be analyzed according to the data, and the internal bottleneck of the host is diagnosed according to the internal network condition.

The invention discloses a synchronous positioning and mapping method and system based on multi-sensor fusion, a storage medium and electronic equipment. The method comprises the following steps: acquiring IMU data, an image and point cloud data measured by a laser radar; performing pre-integration on the IMU data to obtain real-time pose estimation, and performing laser probability updating and visual probability updating based on the real-time pose estimation to obtain an updated local map; a laser point cloud matching residual error, a visual reprojection residual error, an IMU pre-integration residual error and a marginalization residual error are constructed, a residual error equation is constructed based on the laser point cloud matching residual error, the visual reprojection residual error, the IMU pre-integration residual error and the marginalization residual error, a sliding window is adopted for nonlinear optimization, and robot pose estimation is obtained; and visual loopback detection, laser loopback detection and pose global optimization are carried out. According to the method, the calculation rate, the positioning precision and the reliability can be improved in a complex or feature degradation scene.

The invention discloses a USB access safety control system, and particularly relates to the field of USB access electric digital data processing, which comprises an identification module, an isolation module, an encryption continuous transmission module, an auditing module and a kernel interrupt signal triggered by USB physical insertion, and the identification module is used for receiving the kernel interrupt signal and writing the kernel interrupt signal into the interrupt event buffer area, generating a to-be-analyzed interrupt data group, analyzing and generating identification notification information and pushing the identification notification information as a USB access snapshot data frame. Strong binding of USB device access identities, interval control of permission states and traceable closed management of behavior tracks are achieved by constructing a security context vector with device fingerprints, authorization group IDs and insertion timestamps as cores and forming a dynamic association verification path among isolation state judgment, behavior integrity measurement and chain audit account books. The key problems of equipment loopback cheating, permission mismatching, audit failure and the like are solved.

The invention relates to the technical field of point cloud map construction, in particular to a humanoid robot dense point cloud map construction method and system, and the method comprises the steps: obtaining a video image collected when a humanoid robot walks in real time; the gait phase of the humanoid robot in the current frame image in the video image is judged; estimating the camera pose of the current frame image based on the gait phase of the humanoid robot of the current frame image; judging whether the current frame image is a key frame or not based on the camera pose of the current frame image; and performing local mapping and loopback detection according to the key frame to obtain a global dense point cloud map. According to the method, the fuzzy and jittering conditions of the acquired data during mapping of the humanoid robot are effectively improved, a more accurate environment map can be obtained, the operation precision during task execution of the robot is improved, and the collision risk in the advancing process of the humanoid robot is reduced.

The invention relates to the technical field of laser radars, and provides a laser radar angular resolution enhancement method, which comprises the following steps of: obtaining a spatial direction priority according to a vehicle course, road information and target information obtained by a vehicle external sensor so as to dynamically adjust laser pulse density and energy level; scanning data generated after N circles of continuous scanning serve as a composite frame, the starting angle of each circle is shifted, multiphase filtering and coherent superposition are conducted in a digital domain, and the effective angular resolution is obtained. The invention further discloses a laser radar device, the method and the device can adjust the pulse density and energy distribution in the ROI direction in real time according to the dynamic scene of the vehicle, the total pulse number is kept unchanged, and the total power consumption is not increased. The effective horizontal angle resolution is improved through inter-frame phase offset and multi-phase synthesis. A corner reflector and an IMU (Inertial Measurement Unit) are integrated in the laser radar, and phase and ranging errors are corrected in real time through periodic loopback calibration, so that the angle stability is kept.

The invention belongs to the technical field of autonomous navigation and robots, and particularly relates to a three-dimensional laser feature and 4D millimeter wave radar feature fusion SLAM method, which comprises the following steps: 1) an upper computer acquires preprocessed three-dimensional laser point cloud and radar data; 2) performing feature extraction on the processed three-dimensional laser point cloud to obtain a point cloud of a corresponding feature; performing target classification on the processed radar data to obtain 4D millimeter wave radar features; 3) further performing feature extraction on the three-dimensional laser point clouds with corresponding features, extracting square column feature point clouds and cylinder feature point clouds, and classifying the point clouds with different feature types; 4) performing space-time registration on the laser features and the 4D millimeter-wave radar features, and performing association fusion on the features detected by the laser radar and the feature information of the 4D millimeter-wave radar based on a space-time registration result to obtain fused data of the same feature type; 5) performing feature matching and geometric relationship constraint on the fused data with the same feature type to realize point cloud registration; and 6) on the basis of point cloud registration, in combination with an upper computer back-end loopback detection algorithm, optimizing the global pose after loopback succeeds, and outputting the global pose of 6 degrees of freedom under the map coordinate system.