Abstract. We propose a novel direct visual odometry algorithm for
micro-lens-array-based light field cameras. The algorithm calculates a
detailed, semi-dense 3D point cloud of its environment. This is achieved
by establishing probabilistic depth hypotheses based on stereo observations between the micro images of different recordings. Tracking is
performed in a coarse-to-fine process, working directly on the recorded
raw images. The tracking accounts for changing lighting conditions and
utilizes a linear motion model to be more robust. A novel scale optimization framework is proposed. It estimates the scene scale, on the basis
of keyframes, and optimizes the scale of the entire trajectory by filtering over multiple estimates. The method is tested based on a versatile
dataset consisting of challenging indoor and outdoor sequences and is
compared to state-of-the-art monocular and stereo approaches. The algorithm shows the ability to recover the absolute scale of the scene and
significantly outperforms state-of-the-art monocular algorithms with respect to scale drifts.