Abstract
This paper describes a localization module for an autonomous wheelchair. This module includes a combination of various sensors such as odometers, laser scanners, IMU and Doppler speed sensors. Every sensor used in the module features variable covariance estimation in order to yield a final accurate localization. The main problem of a localization module composed of different sensors is the accuracy estimation of each sensor. Average static values are normally used, but these can lead to failure in some situations. In this paper, all the sensors have a variable covariance estimation that depends on the data quality. A Doppler speed sensor is used to estimate the covariance of the encoder odometric localization. Lidar is also used as a scan matching localization algorithm, comparing the difference between two consecutive scans to obtain the change in position. Matching quality gives the accuracy of the scan matcher localization. This structure yields a better position than a traditional odometric static covariance method. This is tested in a real prototype and compared to a standard fusion technique.
Highlights
In a modern robot, the localization sub-module is based on a combination of multiple sensors.Each sensor has its strengths and weaknesses, and only when properly combined can they yield robust localization in every situation.One of the most used fusion techniques is the Kalman filter [1,2] and its variants
The fused localization method follows the scan matching algorithm based on lidar information to obtain an accurate position during the turn
In this paper we present a new method to determine the variable covariance of an encoder based on an odometric sensor
Summary
The localization sub-module is based on a combination of multiple sensors. The key step in the Kalman filter is to characterize the sensors’ covariance, so the fusion can be done properly to obtain an accurate final localization at every point. This paper presents the localization subsystem for the wheelchair, which is based on a sensor fusion scheme. Light Detection and Ranging (LIDAR), with the scan matching technique [7], calculates the difference between two consecutive laser scans to estimate the movement between them This difference is converted to an incremental displacement of the wheelchair, similar to conventional odometry. The method for obtaining the variable covariance of each sensor, the fusion algorithm and the tests for comparing variable and static covariance localization, done with a real prototype, are presented in the sections below.
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