Robot Localization Using Static-Object Weighted Particle Matching
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Solution Overview
Problem
Current localization methods, such as Monte Carlo localization (MCL) and adaptive Monte Carlo localization (AMCL), face challenges in maintaining accuracy and reducing computational resource consumption when environmental changes occur, leading to potential navigation errors and safety issues for autonomous carriers like robots and unmanned aerial vehicles.
Innovation Solution
The proposed localization method incorporates particle filtering with adaptive Monte Carlo localization (AMCL), which includes randomly injecting particles, mapping laser scan points to a global coordinate system, and adjusting weights based on matchness with static objects in a known map to reduce the impact of environmental changes, and employs resampling to maintain estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo localization (MCL) or adaptive Monte Carlo localization (AMCL) is used, then computational resource requirements are low and localization accuracy is high in stable environments, but localization accuracy decreases and time consumption increases when environmental changes occur
Solution Approach 1:
The patent implements dynamic particle injection and weight adjustment mechanisms that adapt to environmental changes. When environmental changes are detected, the system dynamically adjusts the number of particles and their weights, transitioning from static AMCL to a more adaptive particle filtering approach that maintains accuracy in changing conditions.
Solution Approach 2:
The system changes key parameters (particle weights, particle counts, injection probabilities) based on environmental stability assessment. By monitoring environmental changes and adjusting parameters dynamically, the system maintains high localization accuracy across both stable and changing environments while managing computational resources effectively.
2Reliability
If particle filtering with AMCL is used to improve robustness against environmental changes, then localization accuracy improves, but computational resource consumption increases
Solution Approach 1:
The system applies partial particle injection and weight adjustment only when environmental changes are detected, rather than continuously applying full particle filtering. This selective application maintains reliability when needed while minimizing computational overhead during stable periods.
Solution Approach 2:
The system periodically assesses environmental stability and adjusts its localization strategy accordingly. During stable periods, it uses standard AMCL with lower computational resources; when changes are detected, it activates enhanced particle filtering mechanisms to maintain robustness.
3Productivity
If standard AMCL is used, then time consumption is low in stable environments, but error rate increases when environmental changes occur
Solution Approach 1:
The system implements feedback mechanisms that monitor localization confidence and environmental stability. When confidence drops or environmental changes are detected, the system activates corrective particle injection and weight adjustment strategies, preventing navigation errors while maintaining high-speed operation during stable conditions.
Solution Approach 2:
The system performs preliminary environmental assessment and prepares adaptive particle filtering mechanisms in advance. By detecting early signs of environmental changes and pre-adjusting particle parameters, the system prevents localization errors before they affect navigation safety, rather than reacting after errors occur.
Data Source
AI summary
The present disclosure provides a localization method as well as a robot using the same. The method includes: obtaining laser scan points and particles; mapping each of the laser scan points to a global coordinate system based on each of the particles to obtain global boundary points of each of the particles; finding a matching boundary point in the global boundary points by comparing the global boundary points of the particle with points corresponding to static objects in a known map; calculating a distance between the matching boundary point of the particle and the points corresponding to the static objects, and increasing a weight of the matching boundary point if the distance is less than a preset threshold; calculating a weight of the particle by matching the global boundary points of the particle with the known map; and estimating a localization result.