Dynamic Object Tracking and Masking for Visual SLAM

• URL: http://arxiv.org/abs/2008.00072v1
• Date: Fri, 31 Jul 2020 20:37:14 GMT
• Title: Dynamic Object Tracking and Masking for Visual SLAM
• Authors: Jonathan Vincent, Mathieu Labb\'e, Jean-Samuel Lauzon, Fran\c{c}ois Grondin, Pier-Marc Comtois-Rivet, Fran\c{c}ois Michaud
• Abstract summary: In dynamic environments, performance of visual SLAM techniques can be impaired by visual features taken from moving objects. This paper presents a pipeline that uses deep neural networks, extended Kalman filters and visual SLAM to improve both localization and mapping.
• Score: 1.37013665345905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In dynamic environments, performance of visual SLAM techniques can be impaired by visual features taken from moving objects. One solution is to identify those objects so that their visual features can be removed for localization and mapping. This paper presents a simple and fast pipeline that uses deep neural networks, extended Kalman filters and visual SLAM to improve both localization and mapping in dynamic environments (around 14 fps on a GTX 1080). Results on the dynamic sequences from the TUM dataset using RTAB-Map as visual SLAM suggest that the approach achieves similar localization performance compared to other state-of-the-art methods, while also providing the position of the tracked dynamic objects, a 3D map free of those dynamic objects, better loop closure detection with the whole pipeline able to run on a robot moving at moderate speed.

Related papers

• V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting [1.3493547928462395]
  Simultaneous localization and mapping (SLAM) in highly dynamic environments is challenging due to the correlation between moving objects and the camera pose. We propose a robust method, V3D-SLAM, to remove moving objects via two lightweight re-evaluation stages. Our experiment on the TUM RGB-D benchmark on dynamic sequences with ground-truth camera trajectories showed that our methods outperform the most recent state-of-the-art SLAM methods.
  arXiv  Detail & Related papers  (2024-10-15T21:08:08Z)
• DENSER: 3D Gaussians Splatting for Scene Reconstruction of Dynamic Urban Environments [0.0]
  We propose DENSER, a framework that significantly enhances the representation of dynamic objects. The proposed approach significantly outperforms state-of-the-art methods by a wide margin.
  arXiv  Detail & Related papers  (2024-09-16T07:11:58Z)
• Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection [57.883265488038134]
  We propose a hierarchical graph interaction network termed HGINet for camouflaged object detection. The network is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Our experiments demonstrate the superior performance of HGINet compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-27T12:53:25Z)
• 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)
• 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)
• Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios [70.70421502784598]
  RDS-SLAM can build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios.
  arXiv  Detail & Related papers  (2022-10-10T11:03:32Z)
• TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo [55.30992853477754]
  We present TANDEM, a real-time monocular tracking and dense framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of alignments. TANDEM shows state-of-the-art real-time 3D reconstruction performance.
  arXiv  Detail & Related papers  (2021-11-14T19:01:02Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection [78.04869214450963]
  We propose a novel dynamic temporal-temporal network (DSNet) for more effective fusion of temporal and spatial information. We show that the proposed method achieves superior performance than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-09T06:42:30Z)
• 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.