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Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system

  • Dominik Ernst ORCID logo EMAIL logo , Sören Vogel , Ingo Neumann and Hamza Alkhatib
Published/Copyright: May 22, 2024
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Journal of Applied Geodesy
From the journal Journal of Applied Geodesy Volume 18 Issue 4

Abstract

Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs.

Keywords: uncertainty evaluation; uncertainty modeling; multi-sensor system; LiDAR; IMU
Corresponding author: Dominik Ernst, Geodetic Institute, Leibniz University Hannover, Hannover, Germany, E-mail:

Funding source: Deutsche Forschungsgemeinschaft

Award Identifier / Grant number: GRK.2159

  1. Research ethics: Not applicable.

  2. Author contributions: Experiments, Coding, Processing, Writing: DE, HA; Proof-Reading: SV, HA, IN; Project Supervision: IN, HA. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Competing interests: The authors state no competing interests.

  4. Research funding: This work was funded by the German Research, Foundation (DFG) as part of the Research Training Group, i.c.sens [RTG 2159].

  5. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-10-31
Accepted: 2024-05-06
Published Online: 2024-05-22
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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  2. Special Issue on Uncertainty and Quality of Multi-Sensor Systems; Guest Editor: Volker Schwieger
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  4. Development of GPS time-based reference trajectories for quality assessment of multi-sensor systems
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  6. Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system
  7. Guest Editorial
  8. Uncertainty and quality of multi-sensor systems
  9. Original Research Articles
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https://doi.org/10.1515/jag-2023-0098
Keywords for this article
uncertainty evaluation; uncertainty modeling; multi-sensor system; LiDAR; IMU

Articles in the same Issue

  1. Frontmatter
  2. Special Issue on Uncertainty and Quality of Multi-Sensor Systems; Guest Editor: Volker Schwieger
  3. Improving the approximation quality of tensor product B-spline surfaces by local parameterization
  4. Development of GPS time-based reference trajectories for quality assessment of multi-sensor systems
  5. PointNet-based modeling of systematic distance deviations for improved TLS accuracy
  6. Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system
  7. Guest Editorial
  8. Uncertainty and quality of multi-sensor systems
  9. Original Research Articles
  10. Coseismic slip model of the 14 January 2021 Mw 6.2 Mamuju-Majene earthquake based on static and kinematic GNSS solution
  11. Simulation of range code tracking loop for multipath mitigation in NavIC receiver
  12. Exploring ionospheric dynamics: a comprehensive analysis of GNSS TEC estimations during the solar phases using linear function model
  13. A new approach of multi-dimensional correlation as a separability measure of multiple outliers in GNSS applications
  14. Preliminary results of scintillation monitoring at KLEF-Guntur low latitude station using GNSS software defined radio
  15. Evaluating the single-frequency static precise point positioning accuracies from multi-constellation GNSS observations at an Indian low-latitude station
  16. Analysis of ionospheric anomalies before the Fukushima Mw 7.3 earthquake of March 16, 2022
  17. Geomagnetic storm effect on equatorial ionosphere over Sri Lanka through total electron content observations from continuously operating reference stations network during Mar–Apr 2022
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