On Autonomous Spatial Exploration with Small Hexapod Walking Robot using Tracking Camera Intel RealSense T265

In this paper, we report on the deployment of the combination of commercially available off-the-shelf embedded visual localization system and RGB-D camera in an autonomous robotic exploration performed by small hexapod walking robot. Since the multi-legged walking robot is capable of traversing rough terrains, the addressed exploration problem is to create a map of an unknown environment while simultaneously performing the traversability assessment of the explored environment to efficiently and safely reach next navigational waypoints. The proposed system is targeted to run onboard of small multi-legged robots, and therefore, the system design is focused on computationally efficient approaches using relatively lightweight components. Therefore, we take advantages of the recently introduced tracking camera Intel RealSense T265 and RGB-D camera Intel RealSense D435 that are deployed to our developed autonomous hexapod walking robot that is equipped with adaptive locomotion control. Together with the proposed computationally efficient data representation and traversability assessment, the developed system supports onboard mapping and online decision-making within the exploration strategy even on a platform with low computational capabilities. Based on the reported experimental evaluation of the tracking camera, the developed system provides sufficiently accurate localization, and the robot has been able to explore indoor and outdoor environments fully autonomously.

I. Introduction

Spatial robotic exploration is a problem to create a map of the reachable area by a mobile robot. Many approaches, such as those mentioned in the survey [1], address the exploration by extending the idea of the frontier-based exploration introduced in [2]. Frontiers are borders between known and unknown parts of the environment and represent locations towards which robots can be navigated to acquire new information about unexplored parts of the environment [3].