Advanced Sensor Fusion of LiDAR Technologies

Accurate, robust positioning for autonomous vehicles, using data from onboard sensors without relying on external maps. The method integrates inertial measurements with LiDAR point clouds and local maps to determine a vehicle's position. It compensates vehicle motion using inertial data, matches LiDAR points to maps, and probabilistically combines the data sources to optimize positioning.

Associating object detection in a 2D image with 3D point cloud data from multiple sensors to accurately locate and track objects in 3D space. The technique uses neural networks to analyze subsets of sensor data from different modalities associated with object detection. It combines the network outputs to generate probability maps indicating which points in the point clouds are likely to belong to the detected object. This allows associating the object detection with 3D data and generating a more accurate 3D region of interest.

Syncing multiple rotating LiDAR sensors on a vehicle to capture overlapping environments by adjusting their scanning directions and rotations. The system accounts for differences in mounting positions and phases and aligns their scans to combine the sensor data into a coherent representation. This involves adjusting the individual sensor's rotation to scan the same targets simultaneously.

An integrated imaging device combining LiDAR and thermal imaging to overcome limitations of conventional camera and LiDAR systems for applications like autonomous vehicles, and military reconnaissance. The key features are: 1) Co-locating LiDAR and thermal photodetectors on a single focal plane array (FPA) to correlate object detection between the two sensing modes. 2) Utilizing separate wavebands for LiDAR (e.g. NIR) and thermal (e.g. LWIR) to avoid interferences. 3) Configurable readout circuitry to optimize FPA operation between 2D thermal and 3D LiDAR imaging.

System for combining LiDAR and video measurements to generate 3D images of targets and refining the 3D images to account for errors. The system uses LiDAR measurements and video images to resolve the motion trajectory of a target. It then refines the 3D images by reducing the errors in the transformation parameters between video frames.

Method and system to accurately filter background from solid-state roadside LiDAR data to extract road user information for autonomous vehicles. It uses a roadside solid-state LiDAR to extract background frames by aggregating individual channel point clouds. Then in real-time data, channel point clouds are extracted and compared against their corresponding background channel to identify road users. The resulting road user point clouds from each channel are combined into a complete road user point cloud. This filters out the static background and provides accurate road user information for self-driving vehicles.

Perception processing pipeline for object detection in self-driving cars that fuses image semantic data (e.g., semantic segmentation scores) with LiDAR points to improve detection accuracy. The pipeline uses a sequential fusion architecture that accepts LiDAR point clouds and camera images as input and estimates oriented 3D bounding boxes for all relevant object classes. It consists of three stages: 1) semantic segmentation to compute semantic data, 2) fusion to combine the data with LiDAR points, and 3) 3D object detection using a network that takes the fused point cloud as input.

Training an ML model to detect object velocity and center even when training data is sparse. The technique involves using subsets of image and point cloud data associated with object detection to train the ML model. The model outputs velocity and center information that can be used to predict future object positions. The model parameters are adjusted based on differences between predictions and ground truth, improving accuracy.

Calibrating the video and LiDAR subsystems of a 3D imaging system using facial features to improve the accuracy of mapping 3D coordinates to 2D images. The calibration process involves mapping measurements of facial features obtained by each subsystem to align their coordinate systems. This allows combining LiDAR range measurements with video images to generate accurate 3D images of a target.

Estimating vehicle size from LiDAR data in autonomous vehicles to avoid collisions. The technique uses LiDAR data clustering and analysis to estimate object heights. The LiDAR data is processed by associating it with a 2D representation, removing ground points, clustering remaining points to identify objects, and estimating object heights based on the vertical extent and distances between LiDAR beams. The estimated heights are used to control the autonomous vehicle.