Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| Pages | 1398-1405 |
| Number of pages | 8 |
| ISBN (electronic) | 978-1-7281-8526-2 |
| Publication status | Published - 2020 |
| Event | 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Toronto, Canada, Toronto, Canada Duration: 11 Oct 2020 → 14 Oct 2020 |
Publication series
| Name | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
|---|---|
| Volume | 2020-October |
| ISSN (Print) | 1062-922X |
Abstract
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.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Electrical and Electronic Engineering
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2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2020. p. 1398-1405 9283449 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2020-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -