Traversability analysis for off-road environments using locomotion experiments and earth observation data

Matthias Eder*, Raphael Prinz, Florian Schöggl, Gerald Steinbauer-Wagner

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung

Abstract

In recent years, the navigation capabilities of mobile robots in off-road environments have increased significantly, opening up new potential applications in a variety of settings. By accurately identifying different types of terrain in unstructured environments, safe automated navigation can be supported. However, to enable safe path planning and execution, the traversability costs of the terrain types need to be accurately estimated. Such estimations are often performed manually by experts who possess information about the environment and are familiar with the capabilities of the robotic system or using simplified experiments. In this paper, we present an automated pipeline for generating traversability costs that use recorded locomotion data from a realistic experiment and descriptive information on the terrain obtained from earth observation data. The main contribution is that the cost estimation for different terrain types is based on locomotion data obtained in realistic standardized experiments. Moreover, by repeating the experiments with different robot systems we are easily able to reflect the actual capabilities of the systems. Experiments were conducted in an alpine off-road environment to record locomotion data of four different robot systems and to investigate the performance and validity of the proposed pipeline. The recorded locomotion data for the different robots are publicly available at https://robonav.ist.tugraz.at/data/

Originalspracheenglisch
Aufsatznummer104494
FachzeitschriftRobotics and Autonomous Systems
Jahrgang168
DOIs
PublikationsstatusVeröffentlicht - Okt. 2023

ASJC Scopus subject areas

  • Steuerungs- und Systemtechnik
  • Software
  • Allgemeine Mathematik
  • Angewandte Informatik

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