# DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes

arXiv: https://arxiv.org/abs/2205.08207 · HTML: https://mlsanity.com/paper/2205.08207/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 7 of 7)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments](https://mlsanity.com/paper/1705.09479/) (2017)
- [Robust Visual SLAM with Point and Line Features](https://mlsanity.com/paper/1711.08654/) (2017)
- [Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation](https://mlsanity.com/paper/2009.08746/) (2020)
- [Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model](https://mlsanity.com/paper/1907.08388/) (2019)

## Built on by (most cited first, 3 of 3)

- [LiVeR: Lightweight Vehicle Detection and Classification in Real-Time](https://mlsanity.com/paper/2206.06173/) (2022)
- [LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes](https://mlsanity.com/paper/2304.03872/) (2023)
- [SFVO: Decoupled Confidence-Guided Stereo-Flow Visual Odometry with Bidirectional PnP](https://mlsanity.com/paper/2609.21754/) (2026)

## Abstract (arXiv)

Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images. However, in dynamic scenes mainly comprising moving pedestrians, vehicles, etc., there are insufficient robust static point features to enable accurate motion estimation, causing failures when reconstructing robotic motion. In this paper, we proposed DynPL-SVO, a complete dynamic SVO method that integrated united cost functions containing information between matched point features and re-projection errors perpendicular and parallel to the direction of the line features. Additionally, we introduced a textitdynamic textitgrid algorithm to enhance its performance in dynamic scenes. The stereo camera motion was estimated through Levenberg-Marquard minimization of the re-projection errors of both point and line features. Comprehensive experimental results on KITTI and EuRoC MAV datasets showed that accuracy of the DynPL-SVO was improved by over 20% on average compared to other state-of-the-art SVO systems, especially in dynamic scenes.