A robust landmark-based system for vehicle location using low-bandwidth vision

Lin, Luke; Hancock, Thomas R.; Judd, J. Stephen

doi:10.1016/s0921-8890(98)00005-0

Cited by 9 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For navigation, visual examination of the environment is almost a prerequisite, especially for truly autonomous systems as discussed in Von Wichert (1999), in which the importance of a self-organising image recognition system is emphasised. Most of the work in vision-based navigation to date depends upon landmark recognition, in which recognition of specific objects within the environment allows determination of position (Von Wichert 1998, Lin et al 1998a. Other approaches include recognition of particular feature types, such as lane-detection (Baluja and Pomerleau 1997), and the use of a search for the most distinctive features in a scene (Weng and Chen 1998).…”

Section: Roboticsmentioning

confidence: 99%

The state of play in machine/environment interactions

Aitkenhead

McDonald

2006

Artif Intell Rev

View full text Add to dashboard Cite

Due to the breadth of the subject, it is no longer possible to provide a review of all of the work being carried out in the field of Artificial Intelligence. However, a more localised review of research taking place in the overlap between engineering, AI and psychology can be meaningfully performed. We show here that while there have been marked successes in the past few years, there is an identifiable set of 'classic' problems that remain to be solved, and which largely direct the work ongoing in this area. This review aims to discuss the directions being taken at the current time, in particular the developing and maturing possibilities provided by neural networks and evolutionary computation, and by the use of our knowledge of the mind in developing artificial agents capable of mimicking our abilities to interact with the environment.

show abstract

Section: Roboticsmentioning

confidence: 99%

The state of play in machine/environment interactions

Aitkenhead

McDonald

2006

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Scanning a wide area around the robot with a vision system can be done mainly using two different approaches, Correspondence and offprint requests to: Mr A. Rizzi, Department of Information Technology, University of Milano, Via Bramante 65, 26013 Crema (CR), Italy. Email: rizziȰdti.unimi.it a pan-tilt camera or an omnidirectional reflecting surface, whose shape and reflectance properties can differ widely [1][2][3][4][5][6], in this way affecting the visual information acquired. In fact, the surface of the omnidirectional device can either be a perfect mirror that reproduces the visual information around it, or a glazed surface, acting as a low pass filter on the image frequency components.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Autonomous Path-Following with an Omnidirectional Image Sensor

Rizzi

Cassinis

Serana

2002

Neural Comput Applic

View full text Add to dashboard Cite

This paper presents a path-following system implemented with two different types of neural networks, that enables an autonomous mobile robot to return along a previously learned path in a dynamic environment. The path-following is based on data provided by an omnidirectional conical visual system, derived from the COPIS sensor, but with different optical reflective properties. The system uses optical and software processing and a neural network to learn the path, described as a sequence of selected points. In the navigation phase it drives the robot along this learned path. Interesting results have been achieved using low cost equipment. Test and results are presented.

show abstract

“…the robot state vectorx.At each time instant, the motion planning module must determine a reference point on the trajectory, (x ref 9 ; y ref 9 ), which is then used to determine the position and orientation errors so as to correct the robot'To avoid multiple solutions, we use a regularization term that selects the path point,x ref 9 (k) closest to that at the previous time instantx ref9 (k0 1). A signed distance-to-path error d and an orientation [n x n y ] is the normal to the path at the chosen reference point.…”

mentioning

confidence: 99%

Vision-based navigation and environmental representations with an omnidirectional camera

Gaspar

Winters

Santos-Victor

2000

IEEE Trans. Robot. Automat.

248

131

View full text Add to dashboard Cite

This paper proposes a method for the visual-based navigation of a mobile robot in indoor environments, using a single omnidirectional (catadioptric) camera. The geometry of the catadioptric sensor and the method used to obtain a bird's eye (orthographic) view of the ground plane are presented. This representation significantly simplifies the solution to navigation problems, by eliminating any perspective effects.The nature of each navigation task is taken into account when designing the required navigation skills and environmental representations. We propose two main navigation modalities: topological navigation and visual path following.Topological navigation is used for traveling long distances and does not require knowledge of the exact position of the robot but rather, a qualitative position on the topological map. The navigation process combines appearance based methods and visual servoing upon some environmental features.Visual path following is required for local, very precise navigation, e.g., door traversal, docking. The robot is controlled to follow a prespecified path accurately, by tracking visual landmarks in bird's eye views of the ground plane.By clearly separating the nature of these navigation tasks, a simple and yet powerful navigation system is obtained.

show abstract

A robust landmark-based system for vehicle location using low-bandwidth vision

Cited by 9 publications

References 5 publications

The state of play in machine/environment interactions

The state of play in machine/environment interactions

Neural Networks for Autonomous Path-Following with an Omnidirectional Image Sensor

Vision-based navigation and environmental representations with an omnidirectional camera

Contact Info

Product

Resources

About