Pose Graph Optimization for Unsupervised Monocular Visual Odometry

by   Yang Li, et al.

Unsupervised Learning based monocular visual odometry (VO) has lately drawn significant attention for its potential in label-free leaning ability and robustness to camera parameters and environmental variations. However, partially due to the lack of drift correction technique, these methods are still by far less accurate than geometric approaches for large-scale odometry estimation. In this paper, we propose to leverage graph optimization and loop closure detection to overcome limitations of unsupervised learning based monocular visual odometry. To this end, we propose a hybrid VO system which combines an unsupervised monocular VO called NeuralBundler with a pose graph optimization back-end. NeuralBundler is a neural network architecture that uses temporal and spatial photometric loss as main supervision and generates a windowed pose graph consists of multi-view 6DoF constraints. We propose a novel pose cycle consistency loss to relieve the tensions in the windowed pose graph, leading to improved performance and robustness. In the back-end, a global pose graph is built from local and loop 6DoF constraints estimated by NeuralBundler and is optimized over SE(3). Empirical evaluation on the KITTI odometry dataset demonstrates that 1) NeuralBundler achieves state-of-the-art performance on unsupervised monocular VO estimation, and 2) our whole approach can achieve efficient loop closing and show favorable overall translational accuracy compared to established monocular SLAM systems.


UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning

We propose a novel monocular visual odometry (VO) system called UnDeepVO...

LDSO: Direct Sparse Odometry with Loop Closure

In this paper we present an extension of Direct Sparse Odometry (DSO) to...

Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry

Monocular visual odometry consists of the estimation of the position of ...

RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry

Unsupervised learning for monocular camera motion and 3D scene understan...

Incorporating Learnt Local and Global Embeddings into Monocular Visual SLAM

Traditional approaches for Visual Simultaneous Localization and Mapping ...

Deep Auxiliary Learning for Visual Localization and Odometry

Localization is an indispensable component of a robot's autonomy stack t...

H-SLAM: Hybrid Direct-Indirect Visual SLAM

The recent success of hybrid methods in monocular odometry has led to ma...

Please sign up or login with your details

Forgot password? Click here to reset