Details
| Original language | English |
|---|---|
| Title of host publication | 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN) |
| Number of pages | 6 |
| ISBN (electronic) | 979-8-3503-2011-4 |
| Publication status | Published - 6 Dec 2023 |
| Event | 2023 International Conference on Indoor Positioning and Indoor Navigation - Fraunhofer IIS, Nürnberg, Germany Duration: 26 Sept 2022 → 29 Sept 2022 |
Abstract
Keywords
- Error state Kalman filter, Multi-sensor system, Position tracking, IMU, LiDAR, Simulation, simulation, position tracking, multi-sensor system
ASJC Scopus subject areas
- Mathematics(all)
- Control and Optimization
- Physics and Astronomy(all)
- Instrumentation
- Computer Science(all)
- Computer Networks and Communications
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2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN). 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Error State Kalman Filter with Implicit Measurement Equations for Position Tracking of a Multi-Sensor System with IMU and LiDAR
AU - Ernst, Dominik
AU - Vogel, Sören
AU - Neumann, Ingo
AU - Alkhatib, Hamza
N1 - Funding Information: This work was funded by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens [RTG 2159]. Furthermore, this work was supported by the LUH compute cluster, which is funded by the Leibniz Universit¨at Hannover, the Lower Saxony Ministry of Science and Culture (MWK) and the German Research Association (DFG).
PY - 2023/12/6
Y1 - 2023/12/6
N2 - With many applications requiring individuals to share spaces with autonomous systems, not only do accurate positioning solutions become crucial, but also understanding of the uncertainty associated with these systems. For applications like public transportation or logistics, position tracking algorithms need to be evaluated on accuracy and consistency. The selection of the appropriate algorithms heavily relies on the specific requirements of the applications, thus demanding novel algorithms for these new use cases.This paper introduces a novel error state Kalman filter with implicit measurement equations, presenting a solution for new applications. The filter facilitates the fusion of inertial measurement unit (IMU) sensor data with other sensors using arbitrary measurement models. To showcase its effectiveness, the filter is demonstrated by fusing simulated IMU and LiDAR observations for position tracking (complete code on Github). Specifically, the LiDAR points are employed in the update step by minimizing the distances to known planes. Furthermore, the performance of the filter is enhanced by motion compensation through pose interpolation, utilizing the available timestamps. The results are thoroughly discussed in terms of accuracy and consistency, revealing significant improvements by employing pose interpolation. However, the consistency analysis indicates slightly pessimistic results, suggesting the need for further optimizations.
AB - With many applications requiring individuals to share spaces with autonomous systems, not only do accurate positioning solutions become crucial, but also understanding of the uncertainty associated with these systems. For applications like public transportation or logistics, position tracking algorithms need to be evaluated on accuracy and consistency. The selection of the appropriate algorithms heavily relies on the specific requirements of the applications, thus demanding novel algorithms for these new use cases.This paper introduces a novel error state Kalman filter with implicit measurement equations, presenting a solution for new applications. The filter facilitates the fusion of inertial measurement unit (IMU) sensor data with other sensors using arbitrary measurement models. To showcase its effectiveness, the filter is demonstrated by fusing simulated IMU and LiDAR observations for position tracking (complete code on Github). Specifically, the LiDAR points are employed in the update step by minimizing the distances to known planes. Furthermore, the performance of the filter is enhanced by motion compensation through pose interpolation, utilizing the available timestamps. The results are thoroughly discussed in terms of accuracy and consistency, revealing significant improvements by employing pose interpolation. However, the consistency analysis indicates slightly pessimistic results, suggesting the need for further optimizations.
KW - Error state Kalman filter
KW - Multi-sensor system
KW - Position tracking
KW - IMU
KW - LiDAR
KW - Simulation
KW - simulation
KW - position tracking
KW - multi-sensor system
UR - http://www.scopus.com/inward/record.url?scp=85180754435&partnerID=8YFLogxK
U2 - 10.1109/IPIN57070.2023.10332480
DO - 10.1109/IPIN57070.2023.10332480
M3 - Conference contribution
SN - 979-8-3503-2012-1
BT - 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)
T2 - 2023 International Conference on Indoor Positioning and Indoor Navigation
Y2 - 26 September 2022 through 29 September 2022
ER -