The goal of this project is to develop a robust, continuous-time multi-sensor odometry system that can handle multi-rate synchronization.

Visual-inertial odometry with a UAV

Background

Multi-sensor odometry estimates a robot’s full 6-DoF pose by fusing complementary data streams—cameras, LiDAR, IMU, and GNSS—each sampled at its own rate. Representing motion as a continuous-time trajectory, rather than as a sequence of discrete poses, simplifies multi-rate synchronization [1,2]. Each modality breaks down under different conditions: visual–inertial odometry drifts during rapid rotations, abrupt illumination changes, or texture-poor scenes, while LiDAR–inertial odometry loses observability in geometrically featureless environments. Recent work [1] proposes a fusion strategy for robust odometry, but it still relies on tightly synchronized sensors.

Description

The goal of this project is to develop a robust, continuous-time multi-sensor odometry system that can handle multi-rate synchronization. The performance of the resulting approach will be evaluated in comparison with existing multi-sensor odometry approaches.

Work Packages

  • Literature review of work on multi-sensor odometry
  • Literature review of work on continuous-time trajectory
  • Design a multi-sensor fusion strategy
  • Evaluate the performance of the approach in comparison with existing work

Requirements

  • Experience with C++ and ROS

References

  • [1] Cioffi, G., Cieslewski, T., & Scaramuzza, D., “Continuous-time vs. discrete-time vision-based SLAM: A comparative study”, IEEE Robotics and Automation Letters, 2022.
  • [2] Hug, David, Ignacio Alzugaray, and Margarita Chli., “Hyperion–A Fast, Versatile Symbolic Gaussian Belief Propagation Framework for Continuous-Time SLAM”, European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.
  • [3] C. Zheng et al., “FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry”, IEEE Transactions on Robotics, 2024.