Details
| Original language | English |
|---|---|
| Pages (from-to) | 7059-7066 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 7 |
| Issue number | 3 |
| Publication status | Published - 1 Jul 2022 |
Abstract
We present a novel online capable hybrid interval-probabilistic localization method using publicly available 2D building maps. Given an initially large uncertainty for the orientation and position derived from GNSS data, our novel interval-based approach first narrows down the orientation to a smaller interval and provides a set described by a minimal polygon for the position of the vehicle that encloses the feasible set of poses by taking the building geometry into account using 3D Light Detection and Ranging (LiDAR) sensor data. Second, we perform a probabilistic Maximum Likelihood Estimation (MLE) to determine the best solution within the determined feasible set. The MLE is converted into a least-squares problem that is solved by an optimization approach that takes the bounds of the solution set into account so that only a solution within the feasible set is selected as the most likely one. We experimentally show with real data that the novel interval-based localization provides sets of poses that contain the true pose for more than 99% of the frames and that the bounded optimization provides more reliable results compared to a classical unbounded optimization and a Monte Carlo Localization approach.
Keywords
- Interval analysis, Localization, Optimization, Range sensing
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Biomedical Engineering
- Computer Science(all)
- Human-Computer Interaction
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
- Computer Science(all)
- Artificial Intelligence
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In: IEEE Robotics and Automation Letters, Vol. 7, No. 3, 01.07.2022, p. 7059-7066.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Hybrid Interval-Probabilistic Localization in Building Maps
AU - Ehambram, Aaronkumar
AU - Jaulin, Luc
AU - Wagner, Bernardo
PY - 2022/7/1
Y1 - 2022/7/1
N2 - We present a novel online capable hybrid interval-probabilistic localization method using publicly available 2D building maps. Given an initially large uncertainty for the orientation and position derived from GNSS data, our novel interval-based approach first narrows down the orientation to a smaller interval and provides a set described by a minimal polygon for the position of the vehicle that encloses the feasible set of poses by taking the building geometry into account using 3D Light Detection and Ranging (LiDAR) sensor data. Second, we perform a probabilistic Maximum Likelihood Estimation (MLE) to determine the best solution within the determined feasible set. The MLE is converted into a least-squares problem that is solved by an optimization approach that takes the bounds of the solution set into account so that only a solution within the feasible set is selected as the most likely one. We experimentally show with real data that the novel interval-based localization provides sets of poses that contain the true pose for more than 99% of the frames and that the bounded optimization provides more reliable results compared to a classical unbounded optimization and a Monte Carlo Localization approach.
AB - We present a novel online capable hybrid interval-probabilistic localization method using publicly available 2D building maps. Given an initially large uncertainty for the orientation and position derived from GNSS data, our novel interval-based approach first narrows down the orientation to a smaller interval and provides a set described by a minimal polygon for the position of the vehicle that encloses the feasible set of poses by taking the building geometry into account using 3D Light Detection and Ranging (LiDAR) sensor data. Second, we perform a probabilistic Maximum Likelihood Estimation (MLE) to determine the best solution within the determined feasible set. The MLE is converted into a least-squares problem that is solved by an optimization approach that takes the bounds of the solution set into account so that only a solution within the feasible set is selected as the most likely one. We experimentally show with real data that the novel interval-based localization provides sets of poses that contain the true pose for more than 99% of the frames and that the bounded optimization provides more reliable results compared to a classical unbounded optimization and a Monte Carlo Localization approach.
KW - Interval analysis
KW - Localization
KW - Optimization
KW - Range sensing
UR - http://www.scopus.com/inward/record.url?scp=85132728597&partnerID=8YFLogxK
U2 - 10.1109/LRA.2022.3181371
DO - 10.1109/LRA.2022.3181371
M3 - Article
AN - SCOPUS:85132728597
VL - 7
SP - 7059
EP - 7066
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 3
ER -