Laser Point Cloud Motion Compensation via Interpolated Transformations
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Solution Overview
Problem
Current motion compensation methods for laser point cloud data in autopilot systems are inefficient, leading to increased overheads and difficulties in meeting real-time performance requirements due to the need for querying each laser point during the compensation process.
Innovation Solution
A method and apparatus that select a start and end laser point, interpolate their coordinate transformation relations, and use these to transform the coordinates of other laser points to a target time, reducing the number of queries needed and improving processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each laser point is traversed and queried separately during motion compensation, then coordinate transformation accuracy is maintained, but processing overhead increases sharply and real-time performance requirements cannot be met
Solution Approach 1:
The patent segments the laser point cloud into multiple blocks and processes each block independently. Within each block, it further segments the coordinate transformation process by selecting representative points (corner points, center points) to query the transformation relation tree, rather than querying every single point. This segmentation reduces the number of queries while maintaining transformation accuracy for all points in the block through interpolation.
Solution Approach 2:
The patent applies partial action by querying the transformation relation tree for only a subset of points (representative points like corners and centers) rather than all points. The transformation relations obtained from these partial queries are then interpolated to determine transformations for all other points in the block, reducing overall query overhead while maintaining sufficient accuracy.
2Ease of operation
If a transformation relation tree is pre-established for each acquisition time point, then coordinate transformation can be performed, but the complexity of the system increases and overheads sharply increase
Solution Approach 1:
The patent establishes a single transformation relation tree that serves multiple acquisition time points. Instead of creating separate transformation relation trees for each time point (which would increase complexity), one universal tree is built and queried for multiple points by interpolating between the queried results. This multi-functional approach maintains transformation capability while reducing system complexity.
Solution Approach 2:
The transformation relation tree is pre-established once during system initialization or preprocessing, rather than being rebuilt or queried for each individual point during runtime. This preliminary action reduces real-time processing complexity and overhead, as the tree structure is prepared in advance and can be efficiently queried and interpolated during actual motion compensation operations.
3Measurement precision
If coordinate transformation is performed for every laser point to a target time, then motion compensation accuracy is maintained, but processing time increases and stability of the autopilot system is affected
Solution Approach 1:
The patent segments the laser point cloud into multiple blocks and processes each block independently with its own representative points. This segmentation allows parallel processing of different blocks and reduces the total number of transformation queries needed, thereby reducing processing time while maintaining accuracy through block-wise interpolation.
Solution Approach 2:
The patent performs partial transformation by querying the transformation relation tree for only representative points (corners, centers) within each block rather than all points. The transformations for all other points are obtained through interpolation, which maintains motion compensation accuracy while significantly reducing the number of actual transformation operations and processing time.
Data Source
AI summary
A motion compensation method and apparatus applicable to laser point cloud data includes acquiring laser point cloud data of a frame; using an acquisition time of a start point as a target time; interpolating coordinate transformation relations and an end laser point, to obtain coordinate transformation relations of other laser points, and transforming coordinates of the other laser points to the coordinates at the target time. During motion compensation on each frame of laser point cloud data, the coordinate transformation relations corresponding to only the start and end laser points are obtained, and the coordinate transformation relations corresponding to the two points are interpolated to obtain transformation relations corresponding to other laser points, thus performing motion compensation. Therefore, the processing speed is improved and meets high real-time requirements on systems such as autopilot control systems.