Radar Visual Inertial Odometry and Radar Thermal Inertial Odometry: Robust Motion Estimation even in Challenging Visual Conditions

Christopher Doer and Gert F. Trommer
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
[Paper] [Code] [Datasets]

Abstract

In this paper, we propose to fuse radar measurements with Visual Inertial Odometry (RVIO) or Thermal Inertial Odometry (RTIO). FMCW radar sensors enable to estimate the 3D ego velocity independent of the visual conditions. Fusion with VIO or TIO heavily improves the robustness in challenging conditions such as darkness, direct sunlight or fog. Specifically, we propose RRxIO: An extension to the state of the art filter based VIO framework Robust Visual Inertial Odometry (ROVIO) to fuse radar measurements. Scale errors are reduced due to the drift free velocity estimates. In addition, only a small number of features needs to be tracked to achieve good results. RVIO is able to bridge phases of degraded or even no visual features resulting in a low cost system even for poor visual conditions. RTIO is robust even in environments with small temperature gradients and bridges phases of no thermal images caused by Non-Uniformity Corrections (NUCs). We evaluated our system with various experiments in different environments and visual conditions. Comparison to other state of the art dual domain approaches including radar-inertial, visual-inertial and thermal-inertial proves superior performance of our approach regarding accuracy and processing time.

Cite

@INPROCEEDINGS{DoerIros2021,
  author={Doer, Christopher and Trommer, Gert F.},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Rotots and Sytems (IROS)}, 
  title={Radar Visual Inertial Odometry and Radar Thermal Inertial Odometry: Robust Navigation even in Challenging Visual Conditions}, 
  year={2021}}