Lidar Point Cloud Skew Analysis for Relative Velocity
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Solution Overview
Problem
Lidar systems face challenges in accurately detecting and correcting distortions in point cloud frames, particularly due to rolling shutter distortion, which can lead to incorrect object recognition and movement analysis in autonomous vehicles.
Innovation Solution
The implementation of techniques that select and analyze specific points in a point cloud frame to calculate relative velocity, compare depth data from co-located pixels, and correct for distortion by applying a distortion factor, utilizing a database of known object shapes and reference data from RGB or stereo cameras to distinguish between actual object shapes and distortion effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If lidar laser follows a scan pattern to scan the field of regard, then the lidar system can cover the entire field of regard, but there is a time delay between determining depth data at various pixels causing object distortion
Solution Approach 1:
The system performs preliminary actions by capturing multiple images at different time points before final processing. By capturing a sequence of images during the scanning process and identifying corresponding pixels across these images, the system can calculate relative velocity and compensate for motion-induced distortions before generating the final depth map, thus resolving the contradiction between comprehensive field coverage and measurement precision.
2Productivity
If the lidar system scans at high speeds, then scanning efficiency is improved, but object distortion increases due to time delay
Solution Approach 1:
The system implements feedback by calculating relative velocity between the vehicle and objects based on depth data from multiple time points, then using this velocity information to compensate for distortion in the final point cloud. This feedback loop allows high-speed scanning while maintaining object shape accuracy by dynamically adjusting for motion effects detected during the scanning process.
Solution Approach 2:
The system introduces an intermediary processing step where corresponding pixels from multiple images are identified and used to calculate relative velocity. This intermediary calculation serves as a mediator between the high-speed scanning process and the final depth map generation, enabling the system to maintain both high scanning speed and accurate object representation by using the velocity information to correct distortions.
3Measurement precision
If multiple images are captured during scanning, then distortion can be corrected, but processing time and complexity increase
Solution Approach 1:
The system applies segmentation by dividing the field of regard into multiple scan lines and processing corresponding pixels along each scan line independently. This segmentation approach allows the system to handle multiple images captured during scanning by processing them in manageable segments, reducing overall processing complexity while maintaining accurate distortion correction through systematic comparison of corresponding pixels across segmented regions.
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
Enables accurate determination of relative velocity and correction of distortions in point cloud frames, improving object recognition and movement analysis in autonomous vehicles, allowing for faster and more precise navigation.
Implementation Method 1
a scanner determines depth data by measuring the travel time for light beams that are transmitted by the lidar system and scattered or reflected by objects in the field of regard
Data Source
AI summary
A computer-implemented method of determining relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle. The one or more sensors are configured to sense an environment through which the vehicle is moving by following a scan pattern comprising component scan lines. The method includes obtaining, by one or more processors, a point cloud frame based on the sensor data and representative of the environment and identifying, by the one or more processors, a point cloud object within the point cloud frame. The method further includes determining, by the one or more processors, that the point cloud object is skewed relative to an expected configuration of the point cloud object, and determining, by the one or more processors, a relative velocity of the point cloud object by analyzing the skew of the object.