# Does Dynamic-Point Filtering Help When Texture Is Scarce? A Controlled Study of ORB-SLAM2 Front-Ends in Synthetic Indoor Scenes

arXiv: https://arxiv.org/abs/2610.10564 · HTML: https://mlsanity.com/paper/2610.10564/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 25 of 25)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals](https://mlsanity.com/paper/1905.02082/) (2019)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments](https://mlsanity.com/paper/1705.09479/) (2017)
- [Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM](https://mlsanity.com/paper/1911.05603/) (2019)
- [FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow](https://mlsanity.com/paper/2003.05102/) (2020)
- [RGB-D SLAM with Structural Regularities](https://mlsanity.com/paper/2010.07997/) (2020)
- [ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames](https://mlsanity.com/paper/2103.15068/) (2021)
- [VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments](https://mlsanity.com/paper/2102.05965/) (2021)
- [Robust SLAM Systems: Are We There Yet?](https://mlsanity.com/paper/2109.13160/) (2021)
- [GRADE: Generating Realistic And Dynamic Environments for Robotics Research with Isaac Sim](https://mlsanity.com/paper/2303.04466/) (2023)
- [Customizable Perturbation Synthesis for Robust SLAM Benchmarking](https://mlsanity.com/paper/2402.08125/) (2024)
- [VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets](https://mlsanity.com/paper/2504.04457/) (2025)
- [Comparison of modern open-source visual SLAM approaches](https://mlsanity.com/paper/2108.01654/) (2021)
- [RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments](https://mlsanity.com/paper/2207.03539/) (2022)
- [A Benchmark Dataset for Collaborative SLAM in Service Environments](https://mlsanity.com/paper/2411.14775/) (2024)
- [SR-SLAM: Scene-reliability Based RGB-D SLAM in Diverse Environments](https://mlsanity.com/paper/2509.01111/) (2025)
- [DynaPix SLAM: A Pixel-Based Dynamic Visual SLAM Approach](https://mlsanity.com/paper/2309.09879/) (2023)
- [IL-SLAM: Intelligent Line-assisted SLAM Based on Feature Awareness for Dynamic Environments](https://mlsanity.com/paper/2509.02972/) (2025)

## Abstract (arXiv)

Dynamic-point filters are routinely added to feature-based visual SLAM, and several recent systems argue that removing dynamic features can leave too few static features in low-texture regions. So far, these systems have been evaluated only on texture-rich benchmark sequences. We present a controlled study that isolates this interaction. We render synthetic indoor sequences in which surface texture (four levels, quantified by FAST-corner density and image-gradient entropy) and scene dynamics (three levels) are varied factorially along identical camera trajectories, with stereo, RGB-D, ground-truth poses and dynamic masks. On this grid we compare ORB-SLAM2 without filtering, with an optical-flow and epipolar-residual filter (FLOW), and with a multi-view depth-consistency filter (GEOM), and report trajectory error, tracking completeness and surviving static features over five runs. Because the masks give per-keypoint ground truth, we also measure each filter's dynamic-point precision and recall and its static-feature false-removal rate, so that mechanistic explanations can be tested directly. We do not propose a new filter. On 720 runs over 24 sequences, filtering helped mainly in the most dynamic cells; the benefit did not decline monotonically with texture, but at the lowest level filtering reduced tracking completeness, and ORB-SLAM2 never initialised in static L3 scenes. Contrary to our hypothesis, GEOM discarded more static keypoints than FLOW (median FRR 6.8% vs. 1.5% for RGB-D, 19.6% vs. 1.5% for stereo); its RGB-D advantage tracked dynamic-point recall and vanished in stereo mode. Data and code are available at https://github.com/felixxxue/texture-dynamics-slam.