# Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas

arXiv: https://arxiv.org/abs/2009.02672 · HTML: https://mlsanity.com/paper/2009.02672/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment](https://mlsanity.com/paper/2403.18274/) (2024)
- [Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach](https://mlsanity.com/paper/2305.06121/) (2023)
- [MDN-VO: Estimating Visual Odometry with Confidence](https://mlsanity.com/paper/2112.12812/) (2021)
- [DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping](https://mlsanity.com/paper/2403.13714/) (2024)
- [Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines](https://mlsanity.com/paper/2503.02955/) (2025)
- [Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency Transfer](https://mlsanity.com/paper/2308.14332/) (2023)
- [Mapping the ocular surface from monocular videos with an application to dry eye disease grading](https://mlsanity.com/paper/2209.00886/) (2022)
- [Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network](https://mlsanity.com/paper/2303.11725/) (2023)
- [Lost in Tracking Translation: A Comprehensive Analysis of Visual SLAM in Human-Centered XR and IoT Ecosystems](https://mlsanity.com/paper/2411.07146/) (2024)
- [SeeTree -- A modular, open-source system for tree detection and orchard localization](https://mlsanity.com/paper/2504.10764/) (2025)
- [Piggyback Camera: Easy-to-Deploy Visual Surveillance by Mobile Sensing on Commercial Robot Vacuums](https://mlsanity.com/paper/2507.04910/) (2025)

## Abstract (arXiv)

Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which is becoming increasingly mature and accurate, but it tends to be fragile under challenging environments. Comparing with classical geometry-based methods, deep learning-based methods can automatically learn effective and robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks a thorough review of the recent advances of deep learning-based VO (Deep VO). Therefore, this paper aims to gain a deep insight on how deep learning can profit and optimize the VO systems. We first screen out a number of qualifications including accuracy, efficiency, scalability, dynamicity, practicability, and extensibility, and employ them as the criteria. Then, using the offered criteria as the uniform measurements, we detailedly evaluate and discuss how deep learning improves the performance of VO from the aspects of depth estimation, feature extraction and matching, pose estimation. We also summarize the complicated and emerging areas of Deep VO, such as mobile robots, medical robots, augmented reality and virtual reality, etc. Through the literature decomposition, analysis, and comparison, we finally put forward a number of open issues and raise some future research directions in this field.