DeepAI AI Chat
Log In Sign Up

Scale-aware direct monocular odometry

by   Carlos Campos, et al.
University of Zaragoza

We present a framework for direct monocular odometry based on depth prediction from a deep neural network. In contrast with existing methods where depth information is only partially exploited, we formulate a novel depth prediction residual which allows us to incorporate multi-view depth information. In addition, we propose to use a truncated robust cost function which prevents considering inconsistent depth estimations. The photometric and depth-prediction measurements are integrated in a tightly-coupled optimization leading to a scale-aware monocular system which does not accumulate scale drift. We demonstrate the validity of our proposal evaluating it on the KITTI odometry dataset and comparing it with state-of-the-art monocular and stereo SLAM systems. Experiments show that our proposal largely outperforms classic monocular SLAM, being 5 to 9 times more precise, with an accuracy which is closer to that of stereo systems.


Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry

Monocular visual odometry approaches that purely rely on geometric cues ...

Sparse2Dense: From direct sparse odometry to dense 3D reconstruction

In this paper, we proposed a new deep learning based dense monocular SLA...

Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry

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

Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction

Classical monocular Simultaneous Localization And Mapping (SLAM) and the...

Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect

Monocular visual odometry (VO) has seen tremendous improvements in accur...

Learning Depth from Monocular Videos using Direct Methods

The ability to predict depth from a single image - using recent advances...

DF-VO: What Should Be Learnt for Visual Odometry?

Multi-view geometry-based methods dominate the last few decades in monoc...