Rao-Blackwellized Particle Filter with Neural Network using Low-Cost Range Sensor in Indoor Environment

Abstract

Implementation of Rao-Blackwellized Particle Fil-ter (RBPF) in grid-based simultaneous localization and mapping (SLAM) algorithm with range sensors is commonly developed by using sensor with dense measurements such as laser rangefinder. In this paper, a more cost convenient solution was explored where implementation of array of infrared sensors equipped on a mobile robot platform was used. The observation from array of infrared sensors are noisy and sparse. This adds more uncertainty in the implementation of SLAM algorithm. To compensate for the high uncertainties from robot’s observations, neural network was integrated with the grid-based SLAM algorithm. The result shows that the grid-based SLAM algorithm with neural network has better accuracy compared to the grid-based SLAM algorithm without neural network for the aforementioned mobile robot implementation. The algorithm improves the map accuracy by 21% and reduce robot’s state estimate error significantly. The better performance is due to the improvement in accuracy of grid cells’ occupancy value. This affects the importance weight computation in RBPF algorithm hence resulting a better map accuracy and robots state estimate. This finding shows that a promising grid-based SLAM algorithm can be obtained by using merely array of infrared sensors as robot’s observation.