Robot Localization Using Lidar-UWB Particle Filtering Indoors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Indoor mobile robots using lidar for navigation face challenges in large spaces where the measurement distance is beyond 20 meters, limiting their ability to localize accurately in empty areas, leading to reduced navigation capabilities and safety concerns.
Innovation Solution
A particle filtering method combining lidar localization and UWB (ultra-wideband) localization, which uses laser measurement data and UWB measurement data to calculate the matching probability of particles, ensuring accurate localization even in areas with few obstacles, by constraining particles near the UWB localizing location and utilizing laser data for precise positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If single-line lidars with measurement distance within 20 meters are used, then cost requirements are met, but localization in empty spaces beyond 20 meters cannot be achieved
Solution Approach 1:
The patent combines lidar localization and UWB localization into a unified particle filtering framework. The lidar provides precise positioning when obstacles are within 20 meters, while UWB provides positioning capability in empty spaces beyond 20 meters. Both localization systems are integrated through particle filtering to achieve comprehensive coverage throughout the entire indoor environment.
Solution Approach 2:
The patent makes the robot localization system multi-functional by enabling it to operate effectively in both obstacle-rich areas (using lidar) and empty spaces (using UWB). The particle filtering framework universally handles both localization data sources, allowing the robot to maintain positioning capability across all types of indoor environments regardless of obstacle density or distance.
2Measurement precision
If lidar is used for localization, then navigation accuracy is achieved near edges with fixed features, but localization fails in empty spaces without fixed features
Solution Approach 1:
The patent dynamically changes the weighting parameters in the particle filtering algorithm based on the environment. When the robot is in obstacle-rich areas, the lidar measurement likelihood has higher weight for precise localization. When in empty spaces, the UWB measurement likelihood gains higher weight to maintain positioning accuracy. This dynamic parameter adjustment enables the system to adapt to different environmental conditions.
Solution Approach 2:
The particle filtering algorithm serves as an intermediary that reconciles the strengths and weaknesses of lidar and UWB localization. It processes both localization data sources, evaluates their reliability in different contexts, and produces a unified position estimate that leverages the advantages of both systems while compensating for their individual limitations.
Data Source
AI summary
The present disclosure relates to robot technology, and particularly to a robot localization method as well as an apparatus and a robot using the same. The method includes: obtaining a set of particles for localizing the robot; updating a position of each particle in the set of particles based on a preset motion model to obtain the updated position of the particle; obtaining laser measurement data and UWB measurement data; calculating a matching probability of each particle based on the laser measurement data, the UWB measurement data, and the updated position of the particle; and localizing the robot based on the matching probability of each particle. In such a manner, the UWB measurement data is applied to the traditional particle filtering localization algorithm based on laser measurement data so as to enhance the localization precision in large indoor scenes.