Visual-inertial lateral velocity estimation for motorcycles using inverse perspective mapping
Abstract
In this paper, the authors propose a visual-inertial algorithm to estimate the lateral velocity of a motorcycle traveling at high speed along a single-carriageway road. The approach comprises the following steps. First, a monocular camera captures real-time images of the road ahead. Lane markers present in the image are detected and segmented using image processing techniques. Next, a bird's eye view transform is applied, and the dashed center lane markers are isolated. The motion of these markers is computed using an image registration algorithm and is expressed in the motorcycle body frame using orientation estimates from an inertial measurement unit. Finally, this measurement is combined with readings from an accelerometer using a Kalman filter to produce a filtered estimate. The approach was validated using data from simulations of two scenarios created in the BikeSim simulation software suite. In the first scenario, the motorcycle performs a double lane-change across both lanes of a straight road. In the second, the motorcycle navigates an s-shaped bend.