IEEE Robotics and Automation Letters· 2018Q1
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes
- 1,181citations
- Q1SCImago
- 2018year
Short summary
DynaSLAM, a visual SLAM system, successfully detects and inpaints dynamic objects in real-world environments, outperforming standard SLAM baselines in accuracy in highly dynamic scenarios.
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Key points
- DynaSLAM integrates dynamic object detection and background inpainting into visual SLAM systems.
- It supports monocular, stereo, and RGB-D configurations and detects moving objects via multiview geometry and/or deep learning.
- The system enables static map creation and inpainting of areas occluded by dynamic objects.
- DynaSLAM demonstrates superior accuracy compared to standard visual SLAM baselines in highly dynamic environments.
AI-generated from the title and abstract; the full text is not read.
Abstract
The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are the target of several relevant applications like service robotics or autonomous vehicles. In this letter we present DynaSLAM, a visual SLAM system that, building on ORB-SLAM2, adds the capabilities of dynamic object detection and background inpainting. DynaSLAM is robust in dynamic scenarios for monocular, stereo, and RGB-D configurations. We are capable of detecting the moving objects either by multiview geometry, deep learning, or both. Having a static map of the scene allows inpainting the frame background that has been occluded by such dynamic objects. We evaluate our system in public monocular, stereo, and RGB-D datasets. We study the impact of several accuracy/speed trade-offs to assess the limits of the proposed methodology. DynaSLAM outperforms the accuracy of standard visual SLAM baselines in highly dynamic scenarios. And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.
The authors' abstract, as published at the source. IEEE Robotics and Automation Letters, 2018 · DOI ↗
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Field: Aerospace Engineering
Aerospace EngineeringEngineering