UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning

Abstract

We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVO: one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVO by using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.

arXiv comment6 pages, 6 figures, Accepted by ICRA18. Video: (https://www.youtube.com/watch?v=5RdjO93wJqo) Website: (http://senwang.gitlab.io/UnDeepVO/)

References in corpus (4)

• DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
• DeMoN: Depth and Motion Network for Learning Monocular Stereo
• Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
• Unsupervised Learning of Depth and Ego-Motion from Video

Cited by in corpus (10)

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• Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
• D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
• DF-VO: What Should Be Learnt for Visual Odometry?
• Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
• Image Based Camera Localization: an Overview
• AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
• Training Deep SLAM on Single Frames
• Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles
• Guided Feature Selection for Deep Visual Odometry