Particle Filter Pose Estimation Using Map Consistency Checks
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Solution Overview
Problem
Existing grid-based SLAM algorithms for mobile robots face challenges in accurately estimating pose using low-accuracy sensors, leading to position errors and increased calculation time.
Innovation Solution
The method employs a particle filter that adjusts weights using a hill climbing technique based on the robot's heading angle and performs map consistency checks to improve pose estimation accuracy, even with low-accuracy sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a grid-based SLAM algorithm uses a particle filter with standard weight calculation, then the pose estimation can be performed, but the accuracy is insufficient when using low-accuracy sensors
Solution Approach 1:
The patent implements a feedback mechanism by performing map consistency checks on particles and adjusting weights based on the results. Particles that produce maps inconsistent with previously built maps have their weights reduced, creating a feedback loop that corrects for sensor inaccuracies and improves pose estimation accuracy.
Solution Approach 2:
The patent changes the weight parameter of particles based on map consistency check results. By dynamically adjusting the weight parameter according to how well each particle's predicted map matches the actual map, the system compensates for low sensor accuracy and improves overall pose estimation precision.
2Measurement precision
If the particle filter processes all particles with equal weight, then the calculation is simple, but the pose estimation accuracy deteriorates
Solution Approach 1:
The patent applies local quality by treating each particle differently based on its individual map consistency with the previously built map. Instead of uniform weight treatment, each particle receives a customized weight adjustment based on its specific performance in the map consistency check, thereby improving overall estimation accuracy.
3Measurement precision
If high-accuracy sensors are used to improve map quality, then the pose estimation accuracy improves, but the cost and device complexity increase
Solution Approach 1:
The patent replaces expensive high-accuracy sensors with a computational approach using cheaper, lower-accuracy sensors. Instead of investing in high-quality hardware, the system uses algorithmic corrections through map consistency checks and dynamic weight adjustments to achieve the same accuracy goals, effectively substituting computational resources for hardware quality.
Data Source
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Figure 3A
AI summary
Disclosed is a method, apparatus, and medium for estimating a pose of a moving robot using a particle filter. A method for estimating a pose of a moving robot using a particle filter according to an embodiment of the invention includes a detecting a change in pose of the mobile robot and calculating a pose of the current particle by applying the detected change in pose to the previous particle (S100), predicting the probability of the pose of the current particle and obtaining a weight of the current particle (S120) on the basis of range data obtained by a sensor and map information, resampling the current particle on the basis of the weight, and adjusting the weight in consideration of an error of the sensor (S140).