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Title: Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling
Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-frame pose estimation. In this paper, we present a self-supervised learning method for VO with special consideration for consistency over longer sequences. To this end, we model the long-term dependency in pose prediction using a pose network that features a two-layer convolutional LSTM module. We train the networks with purely self-supervised losses, including a cycle consistency loss that mimics the loop closure module in geometric VO. Inspired by prior geometric systems, we allow the networks to see beyond a small temporal window during training, through a novel a loss that incorporates temporally distant (e.g., O(100)) frames. Given GPU memory constraints, we propose a stage-wise training mechanism, where the first stage operates in a local time window and the second stage refines the poses with a "global" loss given the first stage features. We demonstrate competitive results on several standard VO datasets, including KITTI and TUM RGB-D.  more » « less
Award ID(s):
1755785
NSF-PAR ID:
10315232
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
European Conference on Computer Vision
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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