TY - GEN
T1 - Global localization for future space exploration rovers
AU - Boukas, Evangelos
AU - Polydoros, Athanasios S.
AU - Visentin, Gianfranco
AU - Nalpantidis, Lazaros
AU - Gasteratos, Antonios
PY - 2017
Y1 - 2017
N2 - In the context of robotic space exploration the problem of autonomous global or absolute localization remains unsolved. Current rovers require human in the loop approaches to acquire global positioning. In this paper we assess this problem by refining our previous work in a way that advances the performance of the system while making the procedure feasible for real implementation on rovers. A map of semantic landmarks (the Global Network - GN) is extracted on an area that the rover traverses prior to the mission and, during the exploration, a Local Network (LN) is built and matched to estimate rover’s global location. We have optimized several aspects of the system: the motion estimation, the detection and classification –by benchmarking several classifiers– and we have tested the system in a Mars like scenario. With the aim to achieve realistic terms in our scenario a custom robotic platform was developed, bearing operation features similar to ESA’s ExoMars. Our results indicate that the proposed system is able to perform global localization and converges relatively fast to an accurate solution.
AB - In the context of robotic space exploration the problem of autonomous global or absolute localization remains unsolved. Current rovers require human in the loop approaches to acquire global positioning. In this paper we assess this problem by refining our previous work in a way that advances the performance of the system while making the procedure feasible for real implementation on rovers. A map of semantic landmarks (the Global Network - GN) is extracted on an area that the rover traverses prior to the mission and, during the exploration, a Local Network (LN) is built and matched to estimate rover’s global location. We have optimized several aspects of the system: the motion estimation, the detection and classification –by benchmarking several classifiers– and we have tested the system in a Mars like scenario. With the aim to achieve realistic terms in our scenario a custom robotic platform was developed, bearing operation features similar to ESA’s ExoMars. Our results indicate that the proposed system is able to perform global localization and converges relatively fast to an accurate solution.
UR - http://www.scopus.com/inward/record.url?scp=85031779883&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68345-4_8
DO - 10.1007/978-3-319-68345-4_8
M3 - Article in proceeding
AN - SCOPUS:85031779883
SN - 9783319683447
VL - 10528 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 86
EP - 98
BT - 11th International Conference on Computer Vision Systems (ICVS 2017)
PB - Springer VS
T2 - 11th International Conference on Computer Vision Systems, ICVS 2017
Y2 - 10 July 2017 through 13 July 2017
ER -