Reliable frame-to-frame motion estimation for vehicle-mounted surround-view camera systems

Yifu Wang, Kun Huang, Xin Peng, Hongdong Li, Laurent Kneip

Abstract: Modern vehicles are often equipped with a surround-view multi-camera system. The current interest in autonomous driving invites the investigation of how to use such systems for a reliable estimation of relative vehicle displacement. Existing camera pose algorithms either work for a single camera, make overly simplified assumptions, are computationally expensive, or simply become degenerate under non-holonomic vehicle motion. In this paper, we introduce a new, reliable solution able to handle all kinds of relative displacements in the plane despite the possibly non-holonomic characteristics. We furthermore introduce a novel two-view optimization scheme which minimizes a geometrically relevant error without relying on 3D point related optimization variables. Our method leads to highly reliable and accurate frame-to-frame visual odometry with a full-size, vehicle-mounted surround-view camera system.