Monocular Ego-motion Estimation with a Compact Omnidirectional Camera

We present a generalization of the Koenderink-van Doorn (KvD) algorithm that allows robust monocular localization with large motion between the camera frames for a wide range of optical systems including omnidirectional systems and standard perspective cameras. The KvD algorithm estimates simultaneously ego-motion parameters, i.e. rotation, translation, and object distances in an iterative way. However due to the linearization of the rotational component of optic flow, the original algorithm fails for larger rotations. We present a generalization of the algorithm to arbitrary rotations that is especially suited for omnidirectional cameras where features can be tracked for long sequences. This reduces the need for vector summation of several individual motion estimates that leads to accumulation of odometry errors. The significant improvement in the performance of the proposed generalized algorithm compared to the original KvD implementation is validated using simulated data. The algorithm is also tested in a real-world experiment with ground-truth data obtained from an external tracking system. The experiment was carried out using a novel compact omnidirectional camera that is designed for small aerial vehicles. It consists of an off-the-shelf webcam that is combined with a reflective surface machined into acrylic glass.