12 results about "3D radar" patented technology

The application discloses a kind of accurate coal blending car loading system and method of coal preparation plant, the system includes warehouse belt, online coal quality detector a, coal bunker, 3D radar volume monitor, coal feeder, belt scale a, loading belt, online coal quality detector b and belt scale b;Online coal quality detector a is installed on warehouse belt, coal bunker is connected downstream of warehouse belt, 3D radar volume monitor is installed in coal bunker, the outlet of coal bunker is connected with coal feeder, the downstream of coal feeder is connected with belt scale a, the downstream of belt scale a is connected with loading belt, the downstream of loading belt is connected with loading train, online coal quality detector b and belt scale b are installed on loading belt.The method compares the coal quality detection result of loading coal sample and loading requirement, combines the coal quantity of loading and the coal quantity demand required for loading, adjusts the opening of coal feeder under coal bunker to adjust the blending ratio of different coal bunkers, and finally ensures that the coal quality of the whole loading train meets the standard.

1. Name of the product in this design: 3D radar scanner. 2. Purpose of this design: Scanning imaging. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design points: Component 1 3D view 1.

The utility model relates to 3D radar material level meter technical field, concretely to a kind of high temperature 3D radar material level meter suitable for garbage incinerator, including material level meter body, the material level meter body is composed of instrument seat, mounting disc and protective shell, the surface of the material level meter body is provided with instrument seat, the outside sleeve of the instrument seat is fixed with mounting disc, the bottom of the instrument seat is fixed with protective shell, the structure of the protective shell is hemispherical structure, the surface of the material level meter body is provided with air inlet rotating mechanism, the inside of the air inlet rotating mechanism includes rotating assembly and air inlet pipeline, one side of the protective shell is provided with cleaning mechanism, the cleaning mechanism is composed of air duct, arc slide frame, return spring, arc slide and elastic scraping strip. The utility model not only improves the heat dissipation effect when 3D radar material level meter is used, and the kinetic energy required when cleaning 3D radar material level meter is saved.

1. Name of the product in this design: 3D radar level scanner. 2. Purpose of this design: For use in 3D radar level gauges. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention discloses a road target detection method that fuses 4D radar and visual sensors, belonging to the field of intelligent transportation environment perception and autonomous driving technology. First, radar point cloud data and camera image data are acquired and processed to obtain radar BEV features, 3D radar occupancy weights, and image features. Then, the radar BEV features and image features are input into an attention-guided depth distribution supervision module. By combining an attention mechanism with depth distribution supervision, a depth probability distribution map is generated. Next, by combining image features, 3D radar occupancy weights, and the depth probability distribution map, image BEV features are generated. Finally, the radar BEV features and image BEV features are input into a high-efficiency gated fusion module. By combining a difference-driven attention mechanism with gated residual fusion, fused BEV features are generated and input into the detection head, outputting the detection result. This invention can improve the accuracy and system stability of 3D road target detection.

The application discloses a target capturing method based on vision and 3D radar, and belongs to the field of data processing, aiming to solve the problem of high precision requirement of 3D laser radar in identifying target objects. In the method, the target object is detected in real time through visual recognition, and when the target object is detected, the central angle thereof is output and transmitted to the 3D laser radar algorithm part through ROS2 topic communication. After the 3D laser radar receives the output angle value a0, data processing is performed to obtain a fifth distance; and then the obtained M fifth distances are sequentially subjected to average filtering, low-pass filtering, Kalman filtering and difference filtering, so that the final distance is obtained. The application can realize real-time capturing and positioning of the target object at a frequency of more than 10 Hz at a low cost, and complete the target object tracking task with long distance, high precision, high reliability and high frame rate. The final distance error obtained by the method is less than 5 cm.

This disclosure provides a mobile robot relocalization method, apparatus, device, and storage medium, relating to the field of localization technology. The method involves acquiring an initial pose estimate of the mobile robot; based on this initial pose estimate and the current frame's 3D radar point cloud, an improved NDT matching algorithm using a neighborhood search strategy for point-voxel association is used to match the target pose with a global point cloud map; the matching score of the target pose is calculated, and if the score is greater than a threshold, the system switches to localization mode; in localization mode, the pose covariance and point cloud matching rate are monitored in real time, and if the covariance exceeds a threshold or the matching rate is lower than a threshold, the system switches back to relocalization mode and reacquires the initial pose. This disclosure uses an improved NDT algorithm, eliminating the need for large amounts of labeled data and GPU computing power, thus reducing deployment costs; and improves the matching success rate and tolerance to initial errors through a neighborhood search strategy, enabling rapid cold start.

This invention discloses a method for loading mixed bagged products of multiple specifications and varieties onto a truck, comprising: Step S1, based on order information, identifying the types and specifications of materials to be loaded, and obtaining their corresponding dimensional information; Step S2, based on 3D radar scanning of the truck compartment data, obtaining the length, width, and height data of the truck compartment; Step S3, according to the material dimensional information, simulating the allocation of the truck compartment space, stacking the materials, and obtaining the stacking height and number of columns for each specification of material; Step S4, using the obtained stacking height and number of columns for each specification of material, iteratively applying the Levenberg-Marquardt algorithm to obtain a solution that satisfies a set minimum error range; Step S5, finally, determining the truck compartment allocation result based on the obtained solution with the set minimum error range, and then calculating the robot's movement path. This invention ensures the consistency of loading height, the stack is stable and not easily collapsed, and disassembly is also more convenient.

The utility model discloses a multi-degree-of-freedom adjustable 3D radar scanning robot, and relates to the technical field of 3D radar scanning robots, the multi-degree-of-freedom adjustable 3D radar scanning robot comprises a fixed base, a multi-degree-of-freedom mechanical arm is installed at the upper end of the fixed base, and the end portion of the multi-degree-of-freedom mechanical arm is fixedly connected with an assembly seat. The fixing plate is connected with the 3D radar scanner, or the fixing plate and the clamp are installed, so that the clamp or the 3D radar scanner and the multi-degree-of-freedom mechanical arm are installed in a sliding mode, in this way, installation is rapid, alignment adjustment does not need to be conducted for a long time, installation is convenient, and through arrangement of the pressing plate, the 3D radar scanner and the multi-degree-of-freedom mechanical arm can be conveniently installed. During installation, a rubber pad needs to be installed at the lower end of the pressing plate, the pressure on the upper end of the assembly plate is increased, and the assembly plate is extruded and fixed, so that the stability of the assembly plate after installation can be improved, and the stability of the 3D radar scanner during use is ensured.

This application relates to the field of data processing technology and provides a map building method, apparatus, electronic device, and computer program product. In this method, during map building, 2D point cloud data acquired by 2D radar and 3D point cloud data acquired by 3D radar are acquired. Based on the 2D point cloud data, a first global pose is determined, and based on the 3D point cloud data, a 3D map is generated and a reference map is determined. Subsequently, based on the reference map and the 2D point cloud data, the first global pose is updated to obtain a second global pose. Based on the second global pose and the 2D point cloud data, a 2D map is generated. The reference map is determined using highly stable 3D point cloud data, and the global pose determined based on the 2D point cloud data is corrected based on the reference map. Therefore, based on the highly accurate global pose and 2D point cloud data, a clear and noise-free 2D map can be generated, improving the quality of the 2D map and ensuring consistency between the 2D and 3D maps.

This invention discloses a machine learning-based 3D radar positioning anti-interference method, belonging to the field of radar signal processing and machine learning technology. The method includes: acquiring multiple frames of radar echo signals, extracting local point cloud fragments of the obscured target and the global point cloud of the unobscured reference target; inputting the local point cloud fragments and the global point cloud into a spatiotemporal attention graph neural network, calculating the implicit structural association weight between them through a cross-target attention module; transferring geometric features from the global point cloud to the local point cloud fragments based on this weight, generating a complete implicit neural field representation of the obscured target; and using this representation to perform raycasting decoding on the three-dimensional spatial position of the obscured target to obtain the anti-interference 3D radar positioning coordinates. This invention can effectively utilize the global structural information of the unobscured target to recover the complete geometry of the obscured target, improving the positioning accuracy and robustness of radar under obscuration interference.

This invention provides a split-type multi-frequency reconfigurable 3D radar detection system and method. Multiple independently frequency-selectable antenna array modules are flexibly reconfigured through a standardized combination interface. Combined with a distributed main control module, inter-module collaborative control and data fusion are achieved, constructing a modular detection architecture with configurable frequencies, variable form, and adjustable deployment. This architecture supports switching between various operating modes, such as single-module handheld fine detection and multi-module vehicle-mounted rapid survey. A multi-axis robotic arm precisely adjusts the relative positions and detection angles of the modules, ensuring spatiotemporal synchronization and consistency of multi-frequency data. Combined with high-precision positioning and 3D reconstruction algorithms, high-precision 3D images of underground targets are generated. This invention overcomes the limitations of traditional radar frequency fixation and structural rigidity, balancing detection depth and resolution, and significantly improving equipment portability, scalability, and environmental adaptability.