2D Laser Point Cloud Filtering for Tilted Robot Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Two-dimensional laser point clouds used in mobile robots for mapping and localization can cause localization errors when the robot tilts, as part of the point cloud may hit the floor, leading to incorrect feature matching with the global map.
Innovation Solution
A filtering method for two-dimensional laser point clouds is introduced, where the pitch and roll angles of the mobile robot are used to determine a target tilt direction and angle. An angular filtering interval is then calculated based on these angles, allowing the robot to filter out point clouds that hit the floor due to tilting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 2D laser point clouds are used for mapping and localization in mobile robots, then mapping and localization functions are achieved, but localization errors occur when the robot tilts and point clouds hit the floor
Solution Approach 1:
The system performs preliminary filtering of 2D laser point clouds by calculating the robot's tilt angle and determining an angular filtering interval before localization processing. This preliminary action removes point clouds that would cause localization errors due to floor reflections, ensuring only valid point clouds are used for matching with the global map.
Solution Approach 2:
The system changes the parameter of point cloud selection by introducing angular filtering based on tilt angle. Instead of using all detected point clouds, the system dynamically adjusts the angular filtering interval according to the robot's tilt angle, thereby changing which point clouds are considered valid for localization.
2Measurement precision
If point clouds hitting the floor are filtered out to reduce localization errors, then localization accuracy improves, but processing complexity increases
Solution Approach 1:
The filtering process uses simple parameter changes based on the robot's tilt angle to determine the angular filtering interval. This approach maintains low computational complexity while achieving precise filtering, as it only requires calculating the tilt angle and applying a simple angular range filter rather than complex point cloud analysis.
Data Source
AI summary
A filtering method for a two-dimensional laser point cloud includes: acquiring a pitch angle and a roll angle of a mobile robot when the mobile robot is collecting a two-dimensional laser point cloud; determining a target tilt direction corresponding to the mobile robot and a target tilt angle corresponding to the two-dimensional laser point cloud according to the pitch angle and the roll angle; determining an angular filtering interval according to the target tilt direction and the target tilt angle, wherein the angular filtering interval is an angle interval corresponding to the laser point cloud to be filtered out; and filtering, by the mobile robot, the two-dimensional laser point cloud based on the angular filtering interval.