Improved grid mapping technology based on Rao-Blackwellized particle filters and the gradient descent algorithm

Abstract

Recently, the Rao-Blackwellized particle filter (RBPF) has been used to solve the problem of simultaneous localization and mapping (SLAM). Using the odometer information of robot to calculate the proposed distribution requires a number of sampled particles, which increases the calculation complexity in the RBPF operation. In this paper, we integrate the odometer measurement and sensor observation into the proposed distribution, effectively reducing the particle sample scale. To reduce the inconsistency in the map model caused by the cumulative error of the odometer information of robot, we applied a gradient descent algorithm to fuse the sensor data to obtain the real-time attitude angle. This combination method, based on the robot operation system (ROS), runs on a platform of self-built mobile robot equipped with a laser rangefinder. The experimental results show that this method can realize the online real-time high-precision grid map which provides a new approach for robot navigation and SLAM.

Adaptive path planning for unknown environment monitoring

“…Make a 2D grid map with a robot stance [50]. In essence, the fundamental issues in map learning are listed as what place to steer a robot on a mission of self-exploration, how to handle noise in posture estimate and observations, and how to manage with ambiguity and how to interpret it data from sensors, how to effectively manage a team of mobile robots and variations in the echo system over time [59]. The mapping flow procedure is depicted in Fig.…”