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SLAM for autonomous planetary rovers with global localization
Author(s) -
Geromichalos Dimitrios,
Azkarate Martin,
Tsardoulias Emmanouil,
Gerdes Levin,
Petrou Loukas,
Perez Del Pulgar Carlos
Publication year - 2020
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21943
Subject(s) - orbiter , simultaneous localization and mapping , computer science , mars exploration program , artificial intelligence , matching (statistics) , computer vision , particle filter , set (abstract data type) , remote sensing , geography , mobile robot , engineering , robot , mathematics , kalman filter , aerospace engineering , physics , programming language , statistics , astronomy
This paper describes a novel approach to simultaneous localization and mapping (SLAM) techniques applied to the autonomous planetary rover exploration scenario to reduce both the relative and absolute localization errors, using two well‐proven techniques: particle filters and scan matching. Continuous relative localization is improved by matching high‐resolution sensor scans to the online created local map. Additionally, to avoid issues with drifting localization, absolute localization is globally corrected at discrete times, according to predefined event criteria, by matching the current local map to the orbiter's global map. The resolutions of local and global maps can be appropriately chosen for computation and accuracy purposes. Further, the online generated local map, of the form of a structured elevation grid map, can also be used to evaluate the traversability of the surrounding environment and allow for continuous navigation. The objective of this study is to support long‐range low‐supervision planetary exploration. The implemented SLAM technique has been validated with a data set acquired during a field test campaign performed at the Teide Volcano on the island of Tenerife, representative of a Mars/Moon exploration scenario.