Simultaneous localization and mapping using an RGB-D camera for autonomous mobile robot navigation

Abstract

This paper presents the implementation of a simultaneous localization and mapping (SLAM) algorithm for autonomous mobile robot navigation. The proposed implementation uses an RGB-D camera to detect the environment and map an occupancy grid that allows the mobile robot to perform autonomous navigation through the environment. The implementation employs the Robot Operating System (ROS) and the Adaptive Monte Carlo Localization to estimate the mobile robot's current position in the environment with the data retrieved from the RGB-D camera and the odometry data. The mobile robot performs autonomous navigation considering if the robot can safely navigate while avoiding obstacles. Experimental results are presented to validate the implementation.