Flowdometry: An Optical Flow and Deep Learning Based Approach to Visual Odometry

Abstract

Visual odometry is a challenging task related to simultaneous localization and mapping that aims to generate a map traveled from a visual data stream. Based on one or two cameras, motion is estimated from features and pixel differences between frames. Because of the frame rate of the cameras, there are generally small, incremental changes between subsequent frames where optical flow can be assumed to be proportional to the physical distance moved by an egocentric reference, such as a camera on a vehicle. In this paper, a visual odometry system called Flowdometry is proposed based on optical flow and deep learning. Optical flow images are used as input to a convolutional neural network, which calculates a rotation and displacement for each image pixel. The displacements and rotations are applied incrementally to construct a map of where the camera has traveled. The proposed system is trained and tested on the KITTI visual odometry dataset, and accuracy is measured by the difference in distances between ground truth and predicted driving trajectories. Different convolutional neural network architecture configurations are tested for accuracy, and then results are compared to other state-of-the-art monocular odometry systems using the same dataset. The average translation error from the Flowdometry system is 10.77% and the average rotation error is 0.0623 degrees per meter. The total execution time of the system per optical flow frame is 0.633 seconds, which offers a 23.796x speedup over state-of-the-art methods using deep learning.