Mobile Robot Localization Using Map-Matching Pose Uncertainty
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Solution Overview
Problem
Existing localization methods for mobile robots, particularly in outdoor environments, face challenges with odometry errors accumulating over long distances and inaccuracies in map-based localization due to environmental changes and repetitive structures, leading to reduced localization accuracy.
Innovation Solution
A mobile robot system that estimates the uncertainty of map matching poses by using a point cloud sensor, a matching pose calculation part, and an uncertainty estimation part to evaluate the reliability of map matching poses, integrating complementary measurements like IMU, GNSS, and LiDAR odometry for precise localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR-based map matching is used for localization, then global pose accuracy is improved, but reliability deteriorates in environments with insufficient features or repetitive structures
Solution Approach 1:
The system calculates uncertainty values for map-matching poses and uses this uncertainty information as feedback to evaluate reliability. When uncertainty exceeds a threshold, the system identifies the pose as unreliable and excludes it from further processing, preventing erroneous localization results in challenging environments
Solution Approach 2:
The patent changes the parameter representation by computing uncertainty metrics (covariance values) alongside pose estimates. This additional parameter enables the system to dynamically assess reliability based on environmental conditions, allowing it to adapt to situations with insufficient or repetitive features
2Measurement precision
If map-based localization is used to determine global pose, then localization accuracy is improved, but adaptability deteriorates when environmental changes occur after map creation
Solution Approach 1:
The uncertainty calculation provides feedback about how well the current environment matches the stored map. When environmental changes occur, the uncertainty increases, triggering the system to reject the map-matching result and seek alternative localization methods, thus adapting to new conditions
Solution Approach 2:
The system dynamically adjusts its localization strategy based on real-time uncertainty assessment. Instead of rigidly relying on pre-created maps, it can switch to other pose estimation methods when uncertainty indicates environmental changes, making the system flexible and adaptive
3Measurement precision
If odometry methods are used for short-term localization, then measurement precision is improved, but reliability deteriorates over long travel distances due to error accumulation
Solution Approach 1:
The patent merges odometry-based pose estimates with map-matching poses, using uncertainty information to weight their relative contributions. This combination allows the system to benefit from the precision of odometry in the short term while using map matching to correct accumulated errors over longer distances
Data Source
AI summary
Proposed is a mobile robot utilizing the uncertainty of map matching poses for localization. The mobile robot includes a point cloud sensor which acquires point cloud data through scanning, a matching pose calculation part which applies a pre-registered point cloud map and the point cloud data to a pre-registered scan-map matching algorithm to calculate a map-matching pose of the mobile robot, and an uncertainty estimation part which estimates uncertainty of the map-matching pose on the basis of a probability distribution of a preset pose space around the map-matching pose. Through this, the uncertainty of the map-matching pose is estimated to evaluate the reliability of the map-matching pose, enabling more precise localization.