Odometry under Interval Uncertainty: Towards Optimal Algorithms, with Potential Application to Self-Driving Cars and Mobile Robots


In many practical applications ranging from self-driving cars to industrial application of mobile robots, it is important to take interval uncertainty into account when performing odometry, i.e., when estimating how our position and orientation (‘pose’) changes over time. In particular, one of the important aspects of this problem is detecting mismatches (outliers). In this paper, we describe an algorithm to compute the rigid body transformation, including a provably optimal sub-algorithm for detecting mismatches.

Reliable Computing (Interval Computations)
Raphael Voges
Raphael Voges
Doctor in Robotics

My research interests include error modeling, sensor fusion, SLAM, state estimation and AI in the context of autonomous driving.