Inverse Perspective Mapping Roll Angle Estimation for Motorcycles

Abstract

This paper presents an image-based approach to estimate the motorcycle roll angle. The algorithm estimates directly the absolute roll to the road plane by means of a basic monocular camera. This means that the estimated roll angle is not affected by the road bank which is often a problem for vehicle observation and control purposes. For each captured image, the algorithm uses a numeric roll loop based on some simple knowledge of the road geometry. For each iteration, a birdeye-view of the road is generated with the inverse perspective mapping technique. Then, a road marker filter associated with the well-known clothoid model are used respectively to track the road separation lanes and approximate them with mathematical functions. Finally, the algorithm computes two distinct areas between the two-road separation lanes. Its performances are tested by means of the motorcycle simulator BikeSim. This approach is very promising since it does not require any vehicle or tire model and is free of restrictive assumptions on the dynamics.

Learning a Curve Guardian for Motorcycles

“…We believe formulating the task as a learning problem and utilizing the advances in CNNs will create a perception system that is able to generalize better for a diverse set of scenarios. Image-based roll estimation for motorcycles has been proposed by [11], [12]. However, again, these approaches used traditional, geometric computer vision techniques with all their shortcomings.…”