This workshop will be held at the IEEE Intelligent Vehicles Symposium (IV) 2026, on June 22nd, 2026, in Michigan, USA.
Location: TBD
Scope and Topics
This tutorial provides a practical introduction to implementing robust state estimators for automated driving systems (ADS). On a broad view, the tutorial is divided into two parts: a proprioceptive observer design for ADS, and an efficient dynamic-aware visual odometry algorithm (DynaNav-SVO). The tutorial discusses the mathematical background, optimization tools, robust observer design principles, graph optimization, multimodal data fusion for autonomous navigation subject to disturbances and uncertainties in onboard perception systems. The tutorial merges theoretical and practical aspects of the:
- Dynamic vehicle model and tire forces
- Sensor fusion of proprioceptive sensory stream and motion models
- Combined-slip effect incorporation in visual-inertial navigation in ADS and field robotics
- Integrating road surface information in state estimation
- Feature-based visual odometry and bundle adjustment
- Handling dynamic scenes with exteroceptive sensors
Agenda
| Time | Talk Title | ||
|---|---|---|---|
| 04:00:00 | Opening ceremony | ||
| 04:10:00 | Introduction of a novel proprioceptive speed/slip estimation approach by including additional states from the wheel dynamics | ||
| 04:30:00 | Developing a wheel-vehicle/robot dynamical model for designing an integrated kinematic-tire-based observer framework for robot’s/vehicle’s state estimation | ||
| 04:50:00 | The design procedure for optimization- and filter-based observers to include the slip states essential for control and safety-critical decision-making in ADS. | ||
| 05:10:00 | Extensive simulation studies and road experiments will be demonstrated. | ||
| 05:50:00 | Refreshments break | ||
| 06:10:00 | Visual odometry: problem formulation and similarities with Simultaneous Localization and Mapping (SLAM)/Structure-from-Motion (SfM) | ||
| 06:30:00 | Feature extraction and correspondence search | ||
| 06:50:00 | Motion estimation as an optimization problem (with theoretical backgrounds) | ||
| 07:10:00 | Static scene segmentation for robust Point-N-Points/Bundle Adjustment | ||
| 07:30:00 | Integration with proprioceptive state estimator for high-slip scenarios (in field robotics and ADS) and under tire force nonlinearities | ||
| 07:50:00 | Closing Ceremony |
Organizers
- Marcelo Contreras, University of Alberta
- Ehsan Hashemi, University of Alberta
Feel free to contact the organizers if you have any question.