Abstract
Pose estimation of multi-camera rig which has not enough overlapping field of views for the stereo, is generally computationally expensive due to the offset of camera center and the bundle adjustment algorithm. We proposed a divide and conquer approach, which reduces the trinocular visual odometry problem to five monocular visual odometry problems, one for each individual camera sequence and two more using features matched temporally from consecutive images from the center to the left and right cameras, respectively. While this approach provides high accuracy over long distances in outdoor environment without requiring any additional sensors, it is computationally expensive, preventing real-time operation. In this paper, we evaluate trading off image resolution and frame rate to speed up computation with accuracy. Results show that scaling images down to a quarter of Full HD resolution can speed up computation by two orders of magnitude, while still providing acceptable accuracy, whereas dropping frames quickly deteriorates performance.
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