Multi-Submap Pose Estimation for Laser SLAM
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Solution Overview
Problem
In laser SLAM systems, pose estimation errors occur due to the reliance on matching current frames with preceding frames, leading to inaccurate localization and mapping, especially on platforms with weak hashrate, resulting in delayed processing and deformation of mapping results, which cannot meet real-time navigation requirements for robots.
Innovation Solution
A method that determines a first estimated pose based on matching scan data with a submap of preceding frames, then refines this estimation by matching with additional submaps to achieve accurate pose estimation, allowing for fast localization and mapping, even on small platforms with weak hashrate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pose estimation relies on matching current frame with preceding frame or submap, then localization and mapping can be performed, but estimation errors occur and accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism by detecting pose estimation errors and performing error correction through matching with multiple submaps. The system continuously monitors the accuracy of pose estimation and adjusts by selecting the best matching submap to correct deviations, ensuring reliable localization despite initial estimation errors.
Solution Approach 2:
Instead of relying on a single submap for pose estimation, the patent performs matching with multiple submaps (excessive action) to ensure accurate localization. This approach of using more submaps than the minimum required helps compensate for errors and improves the reliability of pose estimation.
2Measurement precision
If multiple submaps are matched to correct pose errors, then pose accuracy improves, but processing time increases and real-time performance deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-dividing the map into multiple submaps and organizing them in advance. This preparation allows the system to quickly retrieve and match with relevant submaps when needed, reducing the actual processing time during runtime while maintaining the ability to perform multiple matches for accuracy.
Solution Approach 2:
The patent segments the entire map into multiple smaller submaps, which can be processed independently and in parallel. This segmentation reduces the computational burden on each individual matching operation and enables efficient error correction without requiring processing of the entire map at once.
3Measurement precision
If map is divided into multiple submaps for error correction, then pose estimation accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the map into multiple submaps, which simplifies the matching process by reducing the search space for each operation. While this creates multiple submaps, each individual submap is simpler to process than a single large map, and the segmentation enables parallel processing that offsets the increased number of components.
Solution Approach 2:
The patent uses multiple submaps (excessive action) to improve accuracy, but only activates the necessary number of submaps for each matching operation based on the current frame's position and orientation. This selective usage prevents the system from having to manage all submaps simultaneously, reducing effective complexity.
Data Source
AI summary
A method for localization and mapping is provided. The method includes: determining a first estimated pose of a current frame based on a matching result of scan data of the current frame and a first submap and a pose of the first submap, where the first submap includes scan data of a common-viewing-angle frame; adding the scan data of the current frame into the first submap; determining a plurality of candidate poses around the first estimated pose; matching the scan data of the current frame with the second submap based on each candidate pose and a pose of each second submap to determine a score of each candidate pose, and determining a second estimated pose of the current frame corresponding to the second submap; determining a third estimated pose from the at least one second estimated pose; updating the scan data based on the third estimated pose.