Practical Aspects of Autonomous Exploration with a Kinect2 sensor

TL;DR

This paper discusses practical challenges and solutions in deploying an autonomous exploration framework using Kinect2 sensor data, focusing on SLAM algorithms and real-world environment mapping.

Contribution

It presents a practical exploration framework utilizing RGBD data from Kinect2, highlighting deployment issues and solutions in real indoor and outdoor environments.

Findings

SLAM algorithms can be effectively used with Kinect2 data on embedded systems.

Practical deployment issues include data processing and real-time mapping challenges.

The framework successfully builds 3D environment maps with visual scene information.

Abstract

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.