TY - GEN

T1 - Hierarchic Single Cluster Graph Partitioning

T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

AU - Ehambram, Aaronkumar

AU - Homann, Hanno

AU - Kleinschmidt, Sebastian P.

AU - Ritter, Tobias

AU - Fischer, Nicolas

AU - Wagner, Bernardo

PY - 2020

Y1 - 2020

N2 - Localization and mapping are essential tasks in mobile robotics. A purely odometry-based pose estimation accumulates errors caused by factors such as unequal wheel-diameters or wheel-slippage and is, therefore, inaccurate. The resulting error can be corrected by recognizing previously observed places and constraining the robots' relative poses. A common challenge of such place recognition methods arises from the incorrect association of different places, which can corrupt the resulting pose correction and, consequently, any map update. Therefore, we propose a novel place recognition method that enables the selection of a correct place recognition hypothesis from a set of possible matches.In this work, we make use of the fact that multiple robots' trajectories run parallel in many scenarios, like in warehouse corridors or on public roads. In such situations, place recognition can be regarded as a sequential task along the robots' trajectories. We study the problem of repetitive, periodic and indistinguishable landmarks for place recognition on regularly-spaced guideposts on German rural roads. By interpreting place recognition hypotheses as nodes of a k-partite graph, we introduce our novel selection method, the Hierarchic Single Cluster Graph Partitioning, that enables the robust selection of the correct hypotheses by finding the optimal path within the graph. The selected place recognition information is used to build a map incorporating multiple observations acquired from multiple vehicles.

AB - Localization and mapping are essential tasks in mobile robotics. A purely odometry-based pose estimation accumulates errors caused by factors such as unequal wheel-diameters or wheel-slippage and is, therefore, inaccurate. The resulting error can be corrected by recognizing previously observed places and constraining the robots' relative poses. A common challenge of such place recognition methods arises from the incorrect association of different places, which can corrupt the resulting pose correction and, consequently, any map update. Therefore, we propose a novel place recognition method that enables the selection of a correct place recognition hypothesis from a set of possible matches.In this work, we make use of the fact that multiple robots' trajectories run parallel in many scenarios, like in warehouse corridors or on public roads. In such situations, place recognition can be regarded as a sequential task along the robots' trajectories. We study the problem of repetitive, periodic and indistinguishable landmarks for place recognition on regularly-spaced guideposts on German rural roads. By interpreting place recognition hypotheses as nodes of a k-partite graph, we introduce our novel selection method, the Hierarchic Single Cluster Graph Partitioning, that enables the robust selection of the correct hypotheses by finding the optimal path within the graph. The selected place recognition information is used to build a map incorporating multiple observations acquired from multiple vehicles.

UR - http://www.scopus.com/inward/record.url?scp=85098891637&partnerID=8YFLogxK

U2 - 10.1109/smc42975.2020.9283449

DO - 10.1109/smc42975.2020.9283449

M3 - Conference contribution

SN - 978-1-7281-8527-9

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 1398

EP - 1405

BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Y2 - 11 October 2020 through 14 October 2020

ER -