LIDAR Point Cloud Updating Through Image Consistency Checks
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Solution Overview
Problem
Existing LIDAR systems face challenges in accurately determining the position and orientation of light deflectors, which affects the precision of object detection and navigation in varying environmental conditions such as rain, fog, darkness, and bright light.
Innovation Solution
A LIDAR system with a laser light source, light-sensitive detector, and processor that generates a point cloud and image, compares them for inconsistencies, and adjusts the point cloud to correct for discrepancies, enhancing precision in object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If LIDAR systems use light deflectors to scan the environment, then the field of view coverage is improved, but the precision of determining light deflector position and orientation deteriorates
Solution Approach 1:
The patent introduces an intermediary calibration process that uses known reference objects in the environment to mediate between the light deflector's intended position and its actual position. By comparing expected point cloud data (based on commanded deflector positions) with actual point cloud data (captured from known reference objects), the system calculates correction factors that compensate for positioning errors without requiring direct measurement of the deflector itself.
Solution Approach 2:
The system implements a feedback mechanism where the actual point cloud data captured by the LIDAR is continuously compared with the expected point cloud data generated from commanded light deflector positions. This comparison generates error signals that are used to update and refine the calibration parameters, creating a closed-loop system that progressively improves measurement precision while maintaining wide field of view coverage.
2Adaptability or versatility
If LIDAR systems operate in varying environmental conditions, then the adaptability is improved, but the object detection accuracy deteriorates
Solution Approach 1:
The patent applies preliminary calibration actions before actual object detection operations. The system performs calibration scans using known reference objects to establish baseline correction factors that account for environmental conditions. These pre-established calibration parameters are then applied during subsequent object detection operations, allowing the system to maintain accuracy across varying environmental conditions without requiring recalibration during active detection.
3Reliability
If LIDAR systems generate point cloud data for object detection, then the detection capability is improved, but the inconsistency between point cloud and image representations worsens
Solution Approach 1:
The patent employs parameter changes by adjusting the calibration parameters that map between the light deflector's commanded positions and actual positions. By modifying these calibration parameters based on the comparison between expected and actual point cloud data, the system resolves inconsistencies between point cloud representations and image representations, ensuring that both data types accurately reflect the same physical objects in the environment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of object detection and navigation by correcting inconsistencies in the point cloud, ensuring reliable operation in diverse environmental conditions.
Implementation Method 1
measuring distances to objects by illuminating objects with light and measuring the reflected pulses with a sensor
Implementation Method 2
A light detection and ranging system, (LIDAR a/k/a LADAR) is an example of technology that can work well in differing conditions, by measuring distances to objects by illuminating objects with light and measuring the reflected pulses with a sensor
Implementation Method 3
generate an image representative of at least a portion of the field of view of the LIDAR system based on output signals generated by the at least one light-sensitive detector in response to non-laser light incident upon the at least one-light sensitive detector
Data Source
AI summary
A LIDAR system includes at least one laser light source, at least one light-sensitive detector including a plurality of pixels, and at least one processor configured to cause the at least one laser light source to project laser light toward a field of view of the LIDAR system, generate a point cloud including distance information relative to objects in the field of view of the LIDAR system based on output signals generated by the at least one light-sensitive detector in response to received laser light return signals reflected from the objects in the field of view, and generate an image representative of at least a portion of the field of view of the LIDAR system based on output signals generated by the at least one light-sensitive detector in response to non-laser light incident upon the at least one-light sensitive detector. The processor is configured to compare the point cloud to the image to detect whether inconsistencies exist between representations in the point cloud and corresponding representations in the image of objects from the field of view, and, in response to detection of one or more inconsistencies between representations in the point cloud and corresponding representations in the image of objects from the field of view, adjust one or more aspects of the generated point cloud to provide an updated point cloud.