Robot Fusion Positioning Using Landmark and Grid Map Constraints
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Solution Overview
Problem
Conventional two-dimensional laser radar positioning methods fail to accurately constrain the three degrees of freedom of a robot's pose in environments like long corridors and open spaces, leading to errors in pose estimation, and relying solely on highly reflective landmarks is inadequate for environments requiring high adaptability.
Innovation Solution
A fusion positioning method that combines two-dimensional laser radar data with iterative optimization using landmark maps and two-dimensional grid maps to accurately determine a robot's pose, incorporating sampling, de-distortion of point cloud data, scan matching, and iterative optimization to minimize errors between landmark positions and estimated robot pose.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If two-dimensional laser radar positioning method is used, then the positioning system is simple, but the positioning accuracy deteriorates in long corridor and open space environments
Solution Approach 1:
The patent combines two-dimensional laser radar scan matching with three-dimensional visual landmark recognition to create a fusion positioning system. The 2D laser radar provides continuous pose estimation while 3D landmarks provide absolute position correction, merging the advantages of both methods to achieve accurate positioning in long corridors and open spaces without excessive complexity
Solution Approach 2:
The positioning system is designed to work effectively across multiple environment types (long corridors, open spaces, and general indoor environments) using a unified fusion approach. The system automatically adapts to different spatial configurations by combining 2D scan data with 3D landmark information, providing universal positioning capability without requiring environment-specific configurations
2Measurement precision
If highly reflective landmarks such as reflective pillars or panels are used for auxiliary positioning, then the positioning accuracy is improved, but the adaptability to environments with high requirements for environmental modification deteriorates
Solution Approach 1:
The system uses naturally occurring 3D geometric landmarks (walls, corners, furniture) that exist in most indoor environments, eliminating the need for specialized reflective markers. This approach maintains high positioning accuracy while providing universal adaptability across diverse environments without requiring environmental modifications
Solution Approach 2:
The system leverages the existing 3D geometric structure of the environment itself as positioning references rather than requiring external artificial landmarks. The environmental features serve their dual purpose of both defining the space and providing positioning cues, eliminating the need for separate landmark installation
3Device complexity
If two-dimensional laser radar is used alone, then the device complexity is low, but the ability to constrain three degrees of freedom of robot pose deteriorates
Solution Approach 1:
The patent merges 2D laser radar scan matching (which provides continuous relative pose estimation) with 3D landmark-based positioning (which provides absolute position constraints) to reliably constrain all three degrees of freedom of robot pose. The combination ensures that the robot's position, orientation, and heading are all accurately determined without requiring complex additional sensors
Data Source
AI summary
A fusion positioning method, comprising: sampling an environmental object by means of a two-dimensional laser radar carried by a robot, so as to acquire radar point cloud-related data of the environmental object; performing adjacent matching on the position Pi of a landmark Li in the environmental object on a road sign map and the landmark map, so as to acquire the position Pj of a landmark Lj nearest to the position Pi on the landmark map; performing scan matching on a two-dimensional grid map by using point cloud data, from which distortion is removed, of the environmental object, so as to acquire a global pose of the robot in the two-dimensional grid map, and taking same as a global pose observation value G pose of the robot; and according the position Pi of the landmark Li on the landmark map, the position Pj of the landmark Lj on the landmark map, and an estimated pose and the global pose observation value G pose of the robot, performing an iterative optimization on the pose of the robot until the error E1 and the error E2 are the smallest.