FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow

• URL: http://arxiv.org/abs/2003.05102v1
• Date: Wed, 11 Mar 2020 04:00:49 GMT
• Title: FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow
• Authors: Tianwei Zhang, Huayan Zhang, Yang Li, Yoshihiko Nakamura and Lei Zhang
• Abstract summary: We present a novel dense RGB-D SLAM solution that simultaneously accomplishes the dynamic/static segmentation and camera ego-motion estimation. Our novelty is using optical flow residuals to highlight the dynamic semantics in the RGB-D point clouds.
• Score: 17.040818114071833
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dynamic environments are challenging for visual SLAM since the moving objects occlude the static environment features and lead to wrong camera motion estimation. In this paper, we present a novel dense RGB-D SLAM solution that simultaneously accomplishes the dynamic/static segmentation and camera ego-motion estimation as well as the static background reconstructions. Our novelty is using optical flow residuals to highlight the dynamic semantics in the RGB-D point clouds and provide more accurate and efficient dynamic/static segmentation for camera tracking and background reconstruction. The dense reconstruction results on public datasets and real dynamic scenes indicate that the proposed approach achieved accurate and efficient performances in both dynamic and static environments compared to state-of-the-art approaches.

Related papers

• Learn to Memorize and to Forget: A Continual Learning Perspective of Dynamic SLAM [17.661231232206028]
  Simultaneous localization and mapping (SLAM) with implicit neural representations has received extensive attention. We propose a novel SLAM framework for dynamic environments.
  arXiv  Detail & Related papers  (2024-07-18T09:35:48Z)
• DEMOS: Dynamic Environment Motion Synthesis in 3D Scenes via Local Spherical-BEV Perception [54.02566476357383]
  We propose the first Dynamic Environment MOtion Synthesis framework (DEMOS) to predict future motion instantly according to the current scene. We then use it to dynamically update the latent motion for final motion synthesis. The results show our method outperforms previous works significantly and has great performance in handling dynamic environments.
  arXiv  Detail & Related papers  (2024-03-04T05:38:16Z)
• DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM [5.267859554944985]
  We introduce DDN-SLAM, the first real-time dense dynamic neural implicit SLAM system integrating semantic features. Compared to existing neural implicit SLAM systems, the tracking results on dynamic datasets indicate an average 90% improvement in Average Trajectory Error (ATE) accuracy.
  arXiv  Detail & Related papers  (2024-01-03T05:42:17Z)
• NID-SLAM: Neural Implicit Representation-based RGB-D SLAM in dynamic environments [9.706447888754614]
  We present NID-SLAM, which significantly improves the performance of neural SLAM in dynamic environments. We propose a new approach to enhance inaccurate regions in semantic masks, particularly in marginal areas. We also introduce a selection strategy for dynamic scenes, which enhances camera tracking robustness against large-scale objects.
  arXiv  Detail & Related papers  (2024-01-02T12:35:03Z)
• EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [85.17951804790515]
  EmerNeRF is a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. It simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. Our method achieves state-of-the-art performance in sensor simulation.
  arXiv  Detail & Related papers  (2023-11-03T17:59:55Z)
• DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields [71.94156412354054]
  We propose Dynamic Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields (DynaMoN) DynaMoN handles dynamic content for initial camera pose estimation and statics-focused ray sampling for fast and accurate novel-view synthesis. We extensively evaluate our approach on two real-world dynamic datasets, the TUM RGB-D dataset and the BONN RGB-D Dynamic dataset.
  arXiv  Detail & Related papers  (2023-09-16T08:46:59Z)
• Alignment-free HDR Deghosting with Semantics Consistent Transformer [76.91669741684173]
  High dynamic range imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. Existing methods often focus on the spatial misalignment across input frames caused by the foreground and/or camera motion. We propose a novel alignment-free network with a Semantics Consistent Transformer (SCTNet) with both spatial and channel attention modules.
  arXiv  Detail & Related papers  (2023-05-29T15:03:23Z)
• Real-time SLAM Pipeline in Dynamics Environment [0.0]
  We are presenting a solution which use RGB-D SLAM as well as YOLO real-time object detection to segment and remove dynamic scene and then construct static scene 3D. We gathered a dataset which allows us to jointly consider semantics, geometry, and physics and thus enables us to reconstruct the static scene while filtering out all dynamic objects.
  arXiv  Detail & Related papers  (2023-03-04T00:08:52Z)
• Robust Dynamic Radiance Fields [79.43526586134163]
  Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. We address this robustness issue by jointly estimating the static and dynamic radiance fields along with the camera parameters.
  arXiv  Detail & Related papers  (2023-01-05T18:59:51Z)
• Dynamic Dense RGB-D SLAM using Learning-based Visual Odometry [0.8029049649310211]
  We propose a dense dynamic RGB-D SLAM pipeline based on a learning-based visual odometry, TartanVO. Our pipeline resolves dynamic/static segmentation by leveraging the optical flow output, and only fuse static points into the map.
  arXiv  Detail & Related papers  (2022-05-12T07:11:41Z)
• DOT: Dynamic Object Tracking for Visual SLAM [83.69544718120167]
  DOT combines instance segmentation and multi-view geometry to generate masks for dynamic objects. To determine which objects are actually moving, DOT segments first instances of potentially dynamic objects and then, with the estimated camera motion, tracks such objects by minimizing the photometric reprojection error. Our results show that our approach improves significantly the accuracy and robustness of ORB-SLAM 2, especially in highly dynamic scenes.
  arXiv  Detail & Related papers  (2020-09-30T18:36:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.