Abstract
This paper proposes a method for mobile robot localization in a partially unknown indoor environment. The method fuses two types of range measurements: the range from the robot to the beacons measured by ultrasonic sensors and the range from the robot to the walls surrounding the robot measured by a laser range finder (LRF). For the fusion, the unscented Kalman filter (UKF) is utilized. Because finding the Jacobian matrix is not feasible for range measurement using an LRF, UKF has an advantage in this situation over the extended KF. The locations of the beacons and range data from the beacons are available, whereas the correspondence of the range data to the beacon is not given. Therefore, the proposed method also deals with the problem of data association to determine which beacon corresponds to the given range data. The proposed approach is evaluated using different sets of design parameter values and is compared with the method that uses only an LRF or ultrasonic beacons. Comparative analysis shows that even though ultrasonic beacons are sparsely populated, have a large error and have a slow update rate, they improve the localization performance when fused with the LRF measurement. In addition, proper adjustment of the UKF design parameters is crucial for full utilization of the UKF approach for sensor fusion. This study contributes to the derivation of a UKF-based design methodology to fuse two exteroceptive measurements that are complementary to each other in localization.
Highlights
Localization is one of the major concerns for the navigation of mobile robots, and it is a constant topic of interest in the research in mobile robotics
Whereas many papers compare the variants of Kalman filters (KFs) and particle filters (PFs) [3,6,11,12,13,14,15,19,20,21], the current study focuses on the comparison of the measurement fusion with the results, which use laser range finder (LRF) only or the ultrasonic beacon only
The proposed method is implemented in a manner similar to the “asynchronous MCL” method [32]. If both the LRF and ultrasonic satellites (USATs) measurements are available in an algorithm runtime period, they are simultaneously processed as described in this paper
Summary
Localization is one of the major concerns for the navigation of mobile robots, and it is a constant topic of interest in the research in mobile robotics. PF uses a large number of samples called particles to represent the probabilistic distribution of the estimation and provides robust estimation results It can deal with nonlinear motion models and non-Gaussian uncertainty in robot motion and sensor measurement. Martinelli [15] used a setting that is very similar to our research They compared the localization performance of EKF and UKF, which use the range measured by an LRF and the range from artificial landmarks. They did not fuse both range data. The method does not assume correspondence of the range data to a beacon It uses the UKF approach with non-additive uncertainty in robot motion and measurement.
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