Practical Aspects of Autonomous Exploration with a Kinect2 sensor
This work tackles incremental improvements in robotic exploration for real-world applications, but does not present major breakthroughs.
The paper addresses practical challenges in deploying an autonomous exploration framework using a Kinect2 sensor for mobile robots in real indoor and outdoor environments, focusing on SLAM algorithm performance on embedded hardware.
Exploration of an unknown environment by a mobile robot is a complex task involving solution of many fundamental problems from data processing, localization to high-level planning and decision making. The exploration framework we developed is based on processing of RGBD data provided by a MS Kinect2 sensor, which allows to take advantage of state-of-the-art SLAM (Simultaneous Localization and Mapping) algorithms and to autonomously build a realistic 3D map of the environment with projected visual information about the scene. In this paper, we describe practical issues that appeared during deployment of the framework in real indoor and outdoor environments and discuss especially properties of SLAM algorithms processing MS Kinect2 data on an embedded computer.