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 -