Avoiding moving outliers in visual SLAM by tracking moving objects

Wangsiripitak, Somkiat; Murray, David

doi:10.1109/robot.2009.5152290

Cited by 70 publications

(50 citation statements)

References 18 publications

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“…The problem of deciding whether an object is static or moving has also been addressed by Sola [27] who employed contextual rules for that purpose. Wangsiripitak et al [29] track a single, known 3D object. This information is used to safeguard an independent SLAM method from observations that are not compatible to the rigid world assumption.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Panteleris

Argyros

2015

Computer Vision - ECCV 2014 Workshops

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Abstract. The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.

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Section: Related Workmentioning

confidence: 99%

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Panteleris

Argyros

2015

Computer Vision - ECCV 2014 Workshops

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“…In realtime monocular visual SLAM systems, moving objects have not yet been dealt properly. In our literature survey, we have only found three works on visual SLAM in dynamic environments: a work by Sola [26] and two other recent works of [30] and [13]. Sola [26] does an observability analysis of detecting and tracking moving objects with monocular vision.…”

Section: Related Workmentioning

confidence: 99%

Realtime motion segmentation based multibody visual SLAM

Kundu

Krishna

Jawahar

2010

Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing

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In this paper, we present a practical vision based Simultaneous Localization and Mapping (SLAM) system for a highly dynamic environment. We adopt a multibody Structure from Motion (SfM) approach, which is the generalization of classical SfM to dynamic scenes with multiple rigidly moving objects. The proposed framework of multibody visual SLAM allows choosing between full 3D reconstruction or simply tracking of the moving objects, which adds flexibility to the system, for scenes containing non-rigid objects or objects having insufficient features for reconstruction. The solution demands a motion segmentation framework that can segment feature points belonging to different motions and maintain the segmentation with time. We propose a realtime incremental motion segmentation algorithm for this purpose. The motion segmentation is robust and is capable of segmenting difficult degenerate motions, where the moving objects is followed by a moving camera in the same direction. This robustness is attributed to the use of efficient geometric constraints and a probability framework which propagates the uncertainty in the system. The motion segmentation module is tightly coupled with feature tracking and visual SLAM, by exploring various feed-backs in between these modules. The integrated system can simultaneously perform realtime visual SLAM and tracking of multiple moving objects using only a single monocular camera.

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“…A method for including information about camera pose from monocular visual SLAM into 3D object tracker, had been used to exclude these features from the SLAM computation in [21]. Stereo based SLAMMOT was proposed to solve the observability problem of monocular camera as well as to increase the accuracy of localization, mapping and tracking [12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic feature detection using virtual correction and camera oscillations

Heshmat

Abdellatif

Nakamura

et al. 2014

2014 International Conference on 3D Imaging (IC3D)

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Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of a static environment. In this paper, we relax the severe assumption of a static scene to allow for the detection and deletion of dynamic points. A new "virtual correction" method is introduced which serves to detect the dynamic points by checking the re-projection error of the points before and after the virtual measurement update. It can also recover the erroneously excluded useful features, particularly the distant points which may be deleted because of the change in its position after new measurement observation. Deliberate camera oscillations are also used to improve the VSLAM accuracy and the camera observability. The simulation results showed the effectiveness of the virtual correction in improving the consistency of the VSLAM and the detection of dynamic points and in particular for difficult scenarios.

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Avoiding moving outliers in visual SLAM by tracking moving objects

Cited by 70 publications

References 18 publications

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Realtime motion segmentation based multibody visual SLAM

Dynamic feature detection using virtual correction and camera oscillations

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