LiDAR Point Cloud Augmentation Using Artificial Return Points
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Solution Overview
Problem
Existing autonomous vehicle technologies face challenges in accurately and timely identifying objects in dynamic driving environments, as they rely solely on positive detections from LiDAR sensors and neglect negative detections, leading to incomplete information about the environment.
Innovation Solution
The method involves generating artificial return points based on the presence or absence of return points along radial paths of LiDAR beams, which are then integrated into augmented point cloud data to represent free space, occluded space, and probable free space, enhancing the accuracy and completeness of environmental information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If autonomous vehicles rely solely on positive detections from LiDAR sensors, then the system complexity is reduced, but the completeness and accuracy of environmental information is insufficient
Solution Approach 1:
The system performs preliminary action by generating artificial return points in advance based on the absence of actual return points along radial paths. This predictive approach infers free space regions before they are explicitly detected, thereby compensating for information loss without requiring additional physical sensors or complex post-processing operations.
Solution Approach 2:
The system creates artificial return points as virtual copies that represent probable free space along radial paths where no actual return points were detected. These synthetic data points mirror the structure and format of genuine LiDAR return points, enabling seamless integration into existing point cloud processing pipelines while enriching environmental information.
2Measurement precision
If artificial return points are generated to represent free space, then the accuracy of object detection is improved, but the data processing complexity increases
Solution Approach 1:
The system applies local quality by generating artificial return points with specific characteristics tailored to their spatial context. Each artificial return point is created with properties (such as intensity and range) that reflect the local environmental conditions along its corresponding radial path, thereby enhancing detection accuracy in specific regions without uniformly increasing complexity across the entire point cloud.
Solution Approach 2:
The system implements partial action by generating artificial return points only in specific regions where free space inference is beneficial, rather than uniformly across all radial paths. This selective approach improves object detection accuracy in critical areas while avoiding unnecessary data processing overhead in regions where actual return points already provide sufficient information.
3Reliability
If artificial return points are integrated into point cloud data, then the reliability of navigation systems is enhanced, but the computational load increases
Solution Approach 1:
The system performs preliminary action by pre-generating artificial return points during the point cloud acquisition phase, before navigation computations begin. This advance preparation ensures that free space information is already integrated into the point cloud data structure, allowing navigation systems to utilize this enhanced information without incurring additional computational overhead during time-critical path planning operations.
Data Source
AI summary
Aspects and implementations of the present disclosure relate to augmenting point cloud data with artificial return points. An example method includes: receiving point cloud data comprising a plurality of return points, each return point being representative of a reflecting region that reflects a beam emitted by a sensing system, and generating a plurality of artificial return points based on presence or absence of return points along radial paths of beams emitted from the sensing system.