Computer vision algorithms for autonomous mobile robot map building and path planning

Meikle, S.; Yates, Robert D.

doi:10.1109/hicss.1998.656291

Cited by 5 publications

(2 citation statements)

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“…Extensions to this method include the storing of sensor signatures on each grid cell to save landmark invariants for later attempts at robot localization [70], or to associate with each cell an occupancy probability distribution [143,182]. Other recent contributions on grid-based map building include those by Jennings et al [104], and Meikie et al [139]. Moreover, probabilistic approaches that combine map learning and localization over grid maps include those by Thrun et al [77,191,193].…”

Section: Map Representation Techniquesmentioning

confidence: 99%

Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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This thesis focuses on the various aspects of autonomous environment learning for indoor service robots. Particularly, on landmark extraction from sensor data, autonomous map building, and robot localization.To univocally identify landmarks from sensor data, we study several landmark representations, and the mathematical foundation necessary to extract the features that build them from images and laser range data. The features extracted from just one sensor may not suffice in the invariant characterization of landmarks and objects, pushing for the combination of information from multiple sources. We present a new algorithm that fuses complementary information from two low level vision modules into coherent object models that can be tracked and learned in a mobile robotics context. Illumination conditions and occlusions are the most prominent artifacts that hinder data association in computer vision. By using photogrammetric and geometric constraints we restrict the search for landmark matches in successive images, and by locking our interest in one or a set of landmarks in the scene, we track those landmarks along successive frames, reducing considerably the data association problem. We concentrate on those tools from the geometry of multiple views that are relevant to the computation of initial landmark location estimates for coarse motion recovery; a desirable characteristic when odometry is not available or is highly unreliable.Once landmarks are accurately extracted and identified, the second part of the problem is to use these observations for the localization of the robot, as well as the refinement of the landmark location estimates. We consider robot motion and sensor observations as stochastic processes, and treat the problem from an estimation theoretic point of view, dealing with noise by using probabilistic methods.The main drawback we encounter is that current estimation techniques have been devised for static environments, and that they lack robustness in more realistic situations. To aid in those situations in which landmark observations might not be consistent in time, we propose a new set of temporal landmark quality functions, and show how iii by incorporating these functions in the data association tests, the overall estimationtheoretic approach to map building and localization is improved. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association, together with the spatial compatibility tests already available.Special attention is paid in that the removal of spurious landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature; namely, asymptotic convergence and full correlation.The thesis also gives an in depth analysis of the fully correlated model to localization and map building from a control systems theory point of view. Considering the fact that the Kalman filter is nothing else but an optimal observer,...

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Section: Map Representation Techniquesmentioning

confidence: 99%

Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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“…Nevertheless these obstacles can be overcome, and the end result is that it is possible to design a robust robot navigation scheme which requires a feature recognition system, able to recognise features regardless of their scale on the image plane. (This has been shown in practise , [6]). Exploration of possible sensors which can be used for this task has resulted in this work.…”

Section: Department Of Engineering Sheffield Hallam Universitymentioning

confidence: 68%

Building smarter sensors - lessons learned from computer vision

Meikle

Yates²

Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511)

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This paper describes recent work in the field of computer vision and relates the results to the much broader class of smart sensors. The sensor requirements for an autonomous mobile robot capable of map building and path planning are described. This shows that vision-bused robots often require the ability to automatically recognize objects regardless of the scale (or relative size) of the object. We show that when the sensor input noise is taken into consideration, a conventional CCD array is unable to provide a robust representation of an object, such that the object can be recognised regardless of the scale of the object on the image plane. In contrast to this, biologicnl bused retinal arrays are able to achieve this (within signal limits). The paper concludes with the perspective that all sensor systems are data dependent. This is of little concern if the sensor consists o f a single element, but becomes more important as larger arrays (with broader selectivity) are fabricated. These sensors may have to emulate biological systems, in an analogous manner to a retinal camera.

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Adaptive control of robot manipulators using multiple neural networks

Lee¹,

Lee²

2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)

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Computer vision algorithms for autonomous mobile robot map building and path planning

Cited by 5 publications

References 12 publications

Environment Learning for Indoor Mobile Robots

Environment Learning for Indoor Mobile Robots

Building smarter sensors - lessons learned from computer vision

Adaptive control of robot manipulators using multiple neural networks

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