Multi-Sensor Fusion SLAM for Repeated-Structure Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing SLAM systems face challenges in maintaining robustness and accuracy in diverse environments and during long continuous operations, particularly in environments with repeated structures such as tunnels or corridors.
Innovation Solution
A multi-sensor fusion SLAM system that integrates laser scanning, visual data, and inertial measurements to improve robustness and accuracy. This system includes a laser scanning matching module, a loop closure detection module, and a visual laser image optimization module, which work together to correct accumulated errors and enhance pose calculation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pure lidar SLAM is used, then robustness to environmental changes is improved, but accuracy in environments with repeated structures deteriorates
Solution Approach 1:
The patent combines multiple sensors (lidar, monocular camera, IMU) into a unified SLAM system. The lidar provides robustness to environmental changes, the monocular camera offers visual constraints for loop closure detection, and the IMU provides inertial measurements for pose estimation. This multi-sensor fusion approach resolves the contradiction by leveraging the strengths of each sensor type to compensate for their individual weaknesses in repeated structure environments.
Solution Approach 2:
The system creates a composite sensing approach by integrating data from heterogeneous sensors (optical lidar, visual camera, inertial IMU). This composite multi-sensor system achieves superior performance compared to pure lidar by combining the geometric precision of lidar with the contextual understanding from visual data and the temporal continuity from inertial measurements.
2Measurement precision
If multi-sensor fusion is implemented, then positioning accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a unified multi-sensor fusion SLAM framework that handles lidar, visual, and inertial data within a single optimization system. This universal approach improves positioning accuracy by fusing multiple data sources while managing complexity through integrated processing rather than separate systems.
Solution Approach 2:
The system transforms heterogeneous sensor data into a unified state estimation problem by changing parameters to a common coordinate system and optimization framework. This allows accurate fusion of lidar point clouds, visual features, and IMU measurements while maintaining computational tractability through parameter standardization.
3Measurement precision
If loop closure detection is performed, then accumulated error is corrected, but calculation amount increases
Solution Approach 1:
The system performs preliminary loop closure detection using visual features from the monocular camera before executing full pose graph optimization. By pre-identifying potential loop closures through visual comparison, the system reduces the calculation amount required for comprehensive error correction while maintaining effective accumulated error correction capability.
Solution Approach 2:
The patent uses visual features as an intermediary for detecting loop closures. Instead of directly comparing all lidar scans for loop closure, the system first uses computationally efficient visual feature matching to identify potential loops, then applies more intensive optimization only when needed. This intermediary approach balances error correction effectiveness with computational efficiency.
Data Source
AI summary
A multi-sensor fusion SLAM system and a robot. The system operates on a mobile robot and comprises: a visual inertia module, a laser scanning matching module, a loop closure detection module, and a visual laser image optimization module. According to the multi-sensor fusion SLAM system and the robot, the calculation amount of laser matching constraint optimization can be reduced by using a voxel subgraph so that the pose calculation is more accurate, accumulated errors of long-time operation of the system can be corrected in time by means of sufficient fusion of modules, and the robustness of the system and the accuracy of positioning and mapping are integrally improved.