A 2.5D Vehicle Odometry Estimation for Vision Applications
This addresses pose estimation for autonomous driving systems, but appears incremental as it builds on existing sensor data and models.
The paper tackles the problem of estimating sensor pose on a vehicle for autonomous driving by combining planar odometry from wheel sensors with a suspension model from linear sensors to improve camera pose accuracy, but no concrete numerical results are provided.
This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world, an important topic for autonomous driving systems. Based on a set of commonly deployed vehicular odometric sensors, with outputs available on automotive communication buses (e.g. CAN or FlexRay), we describe a set of steps to combine a planar odometry based on wheel sensors with a suspension model based on linear suspension sensors. The aim is to determine a more accurate estimate of the camera pose. We outline its usage for applications in both visualisation and computer vision.