Exploiting Graph and Geodesic Distance Constraint for Deep Learning-Based Visual Odometry

Abstract

Visual odometry is the task of estimating the trajectory of the moving agents from consecutive images. It is a hot research topic both in robotic and computer vision communities and facilitates many applications, such as autonomous driving and virtual reality. The conventional odometry methods predict the trajectory by utilizing the multiple view geometry between consecutive overlapping images. However, these methods need to be carefully designed and fine-tuned to work well in different environments. Deep learning has been explored to alleviate the challenge by directly predicting the relative pose from the paired images. Deep learning-based methods usually focus on the consecutive images that are feasible to propagate the error over time. In this paper, graph loss and geodesic rotation loss are proposed to enhance deep learning-based visual odometry methods based on graph constraints and geodesic distance, respectively. The graph loss not only considers the relative pose loss of consecutive images, but also the relative pose of non-consecutive images. The relative pose of non-consecutive images is not directly predicted but computed from the relative pose of consecutive ones. The geodesic rotation loss is constructed by the geodesic distance and the model regresses a Lie algebra so(3) (3D vector). This allows a robust and stable convergence. To increase the efficiency, a random strategy is adopted to select the edges of the graph instead of using all of the edges. This strategy provides additional regularization for training the networks. Extensive experiments are conducted on visual odometry benchmarks, and the obtained results demonstrate that the proposed method has comparable performance to other supervised learning-based methods, as well as monocular camera-based methods. The source code and the weight are made publicly available.