Real-time simultaneous localisation and mapping with a single camera

Davison,

doi:10.1109/iccv.2003.1238654

Cited by 1,398 publications

(1,088 citation statements)

References 7 publications

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“…The quality of the recovered trajectories directly affects the performance of attractive higher level tasks such as structure from motion [1], visual odometry [2], concurrent mapping and localization [3], and visual servoing [4]. However, the priorities of the desired tracking behaviour may differ between the particular contexts, since the former two involve larger numbers of "nameless" features, while the latter ones usually focus on fewer but more important landmarks.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

Šegvić

Remazeilles

Chaumette

2006

Computer Vision – ECCV 2006

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Abstract. We consider the problem of tracking a given set of point features over large sequences of image frames. A classic procedure for monitoring the tracking quality consists in requiring that the current features nicely warp towards their reference appearances. The procedure recommends focusing on features projected from planar 3D patches (planar features), by enforcing a conservative threshold on the residual of the difference between the warped current feature and the reference. However, in some important contexts, there are many features for which the planarity assumption is only partially satisfied, while the true planar features are not so abundant. This is especially true when the motion of the camera is mainly translational and parallel to the optical axis (such as when driving a car along straight sections of the road), which induces a permanent increase of the apparent feature size. Tracking features containing occluding boundaries then becomes an interesting goal, for which we propose a multi-scale monitoring solution striving to maximize the lifetime of the feature, while also detecting the tracking failures. The devised technique infers the parts of the reference which are not projected from the same 3D surface as the patch which has been consistently tracked until the present moment. The experiments on real sequences taken from cars driving through urban environments show that the technique is effective in increasing the average feature lifetimes, especially in sequences with occlusions and large photometric variations.The presented work has been performed within the french national project Predit Mobivip (Individual Public Vehicles for Mobility in the City), and within the project Robea Bodega. The authors would also like to acknowledge Prof. Axel Pinz for a helpful discussion on this subject.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

Šegvić

Remazeilles

Chaumette

2006

Computer Vision – ECCV 2006

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“…In Section D.2 we acknowledge related work in the field and point out new contributions of this paper. The contour estimation problem is formulated in Section D. 3, and in Section D. 4 we derive a discretized version of the optimization problem, suitable for implementation. Finally simulations and experimental results are presented for a few test objects in Section D.5 and conclusions are drawn in Section D.6.…”

Section: D1 Introductionmentioning

confidence: 99%

Contour reconstruction using recursive smoothing splines - Algorithms and experimental validation

Karasalo

Piccolo

Kragić

et al. 2009

Robotics and Autonomous Systems

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In this paper, a recursive smoothing spline approach for contour reconstruction is studied and evaluated. Periodic smoothing splines are used by a robot to approximate the contour of encountered obstacles in the environment. The splines are generated through minimizing a cost function subject to constraints imposed by a linear control system and accuracy is improved iteratively using a recursive spline algorithm. The filtering effect of the smoothing splines allows for usage of noisy sensor data and the method is robust with respect to odometry drift. The algorithm is extensively evaluated in simulations for various contours and in experiments using a SICK laser scanner mounted on a PowerBot from ActivMedia Robotics.Keywords: mapping, optimal control, recursive smoothing splines, implementation D.1 IntroductionToday robots are expected to operate in at least partially unknown, open-ended environments, detecting and moving around obstacles, building maps of the environment and localizing their position. The area of autonomous mapping has during the past few years matured and there has been a significant amount of work reported using both visual and laser sensors, [8], [26]. However, most methods are still concerned with detecting and maintaining a dense set of discrete features of the environment that are suitable for navigation and localization without retrieving any specific knowledge about the detailed shape of the encountered objects.In this paper, we consider the problem of estimating representations of objects using a type of continuous closed curves, the recursive periodic smoothing splines. The splines are retrieved from noisy measurements of the true contour. Intended applications include mapping, identification and path planning. It is well known that interpolating splines from noisy measurement data will give a poor result, as the resulting curve will go through every measurement point. Data smoothing has been a classical problem in system and control history [1], [13], [18]. The theory of regular smoothing splines is thoroughly treated in [29] and [30]. It has been further shown in [35] that control theoretic smoothing splines, where the curve is found through minimizing a cost function, act as filters and are better suited for noisy measurements. It is also noted in [27] that smoothing 89

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“…due to occlusions). They use predictive motion models and update them when the reference is again visible [3,4]. Their weakness is, in general, the drift during the absence of a stable reference (usually due to features difficult to recognise after perspective distortions).…”

Section: Introductionmentioning

confidence: 99%

Video-Based Camera Tracking Using Rotation-Discriminative Template Matching

Marimón

Ebrahimi

2008

Communications in Computer and Information Science

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Abstract. This paper presents a video-based camera tracker that combines marker-based and feature point-based cues in a particle filter framework. The framework relies on their complementary performance. Markerbased trackers can robustly recover camera position and orientation when a reference (marker) is available, but fail once the reference becomes unavailable. On the other hand, feature point tracking can still provide estimates given a limited number of feature points. However, these tend to drift and usually fail to recover when the reference reappears. Therefore, we propose a combination where the estimate of the filter is updated from the individual measurements of each cue. More precisely, the marker-based cue is selected when the marker is available whereas the feature point-based cue is selected otherwise. Feature points are dynamically found in scene and used for further tracking. Evaluations on real cases show that the fusion of these two approaches outperforms the individual tracking results. A critical aspect of the feature point-based cue is to robustly recognise the feature points depite rotations of the camera. A novelty of the proposed framework is the use of a rotation-discriminative method to match feature points.

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Real-time simultaneous localisation and mapping with a single camera

Cited by 1,398 publications

References 7 publications

Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

Contour reconstruction using recursive smoothing splines - Algorithms and experimental validation

Video-Based Camera Tracking Using Rotation-Discriminative Template Matching

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