Abstract
This paper presents a novel approach to mobile robot localization using sonar sensors. This approach is based on the use of particle filters. Each particle is augmented with local environment information which is updated during the mission execution. An experimental characterization of the sonar sensors used is provided in the paper. A probabilistic measurement model that takes into account the sonar uncertainties is defined according to the experimental characterization. The experimental results quantitatively evaluate the presented approach and provide a comparison with other localization strategies based on both the sonar and the laser. Some qualitative results are also provided for visual inspection.
Highlights
A crucial requirement for a mobile robot to accomplish useful long term missions is that the robot has some knowledge about its pose with respect to a fixed reference frame
The results provided by the spMCL are significantly better than those obtained when the Iterative Closest Point (ICP) error is used
The two sonar Monte Carlo Localization (sMCL) approaches presented in this paper provide significant improvements in the pose estimates with respect to raw odometry
Summary
A crucial requirement for a mobile robot to accomplish useful long term missions is that the robot has some knowledge about its pose with respect to a fixed reference frame. The process of estimating the robot pose with respect to such fixed reference frame is known as the localization problem. That is why localization strategies usually rely on accurate sensors such as laser range scanners. Most of these sensors are able to provide thousands of readings per second with a sub degree angular resolution. Other sensors, such as standard Time-of-Flight (TOF) ultrasonic range finders, do not have these properties. Standard ultrasonic range finders are only able to provide tenths of readings per second and have angular resolutions one or two orders of magnitude worse than laser scanners
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