2.5D Vehicle Odometry Estimation
This work addresses incremental improvement in vehicle odometry for low-speed surround-view camera systems in ADAS applications.
The paper tackles vehicle pose estimation for ADAS by proposing a 2.5D odometry method that augments planar odometry with a linear suspension model, showing highly accurate estimates compared to existing methods in experiments with DGPS/IMU reference.
It is well understood that in ADAS applications, a good estimate of the pose of the vehicle is required. This paper proposes a metaphorically named 2.5D odometry, whereby the planar odometry derived from the yaw rate sensor and four wheel speed sensors is augmented by a linear model of suspension. While the core of the planar odometry is a yaw rate model that is already understood in the literature, we augment this by fitting a quadratic to the incoming signals, enabling interpolation, extrapolation, and a finer integration of the vehicle position. We show, by experimental results with a DGPS/IMU reference, that this model provides highly accurate odometry estimates, compared with existing methods. Utilising sensors that return the change in height of vehicle reference points with changing suspension configurations, we define a planar model of the vehicle suspension, thus augmenting the odometry model. We present an experimental framework and evaluations criteria by which the goodness of the odometry is evaluated and compared with existing methods. This odometry model has been designed to support low-speed surround-view camera systems that are well-known. Thus, we present some application results that show a performance boost for viewing and computer vision applications using the proposed odometry