Nio Technology (anhui) Co., Ltd.

Arun C S Kumar

Disha Ahuja

Ashwath Aithal

Embodiments include a method for object detection in a Light Detection And Ranging (LiDAR) point cloud, the method comprising: placing, by a navigation system, a plurality of anchor points in a two-dimensional Bird's Eye View (BEV) of spatial points represented in a segmented ground surface representation of objects detected by a LiDAR system; extracting, by the navigation system, one or more features from the two-dimensional BEV of the spatial points; proposing, by the navigation system, one or more regions of the two-dimensional BEV of the spatial points for object detection; and performing, by the navigation system, object detections on anchor points of the plurality of anchor points in the proposed one or more regions of the two-dimensional BEV of the spatial points.

Object detection using local (ground-aware) adaptive region proposals on point clouds

Embodiments include a method for ground surface segmentation on sparse Light Detection And Ranging (LiDAR) point clouds comprising: reading a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; voxelizing the LiDAR point cloud to produce a three-dimensional representation of each of the one or more objects; constructing a maximum height map from the three-dimensional representation of each of the one or more objects, the maximum height map comprising a two-dimensional mapping of spatial points representing each of the one or more objects; performing minimum filtering on the spatial points of the maximum height map; and classifying each spatial point as a ground point or a non-ground point based on the minimum filtering of each spatial point.

Real-time ground surface segmentation algorithm for sparse point clouds

Systems and methods for generating simulated LiDAR data using RADAR and image data are provided. An algorithm is trained using deep-learning techniques such as loss functions to generate simulated LiDAR data using RADAR and image data. Once trained, the algorithm can be implemented in a system, such as a vehicle, equipped with RADAR and image sensors in order to generate simulated LiDAR data describing the system's environment. The simulated LiDAR data may be used by a vehicle control system to determine, generate, and implement modified driving operations.

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Milpitas, CA, United States of America

Arun C S Kumar