Robot vision network based on ceiling map sharing

Yasuaki, Fukutani; Takahashi, Tatsuhiro; Iwahashi, Masahiro; Kimura, Tetsuya; Salbiah, S.; Mokhtar, Norrima

doi:10.1109/amc.2010.5464005

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the common solution in the development of the localization systems is to install cameras to look around the mobile robot to obtain its position [25], [13], [17], some robots use a single camera looking upward [15], [12], [28]. The use of vision from the ceiling has the advantage that images can be considered static and without scaling.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian grid method PCA-based for mobile robots localization in unstructured environments

Rodrigues

Cardeira

Carreira

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

View full text Add to dashboard Cite

This paper presents the experimental validation of a new method for mobile robot global self-localization in unstructured environments, i.e. that does not need any beacons or other artifacts structuring the environment. The method resorts to a PCA-based positioning sensor, filtered in a Bayesian probabilistic grid and combined with linear Kalman filters to estimate the global pose of mobile robots. In the implemented system, the information of the environment is captured only with onboard sensors installed in a differential drive robot: encoders, compass, and 2D depth sensor pointed to the ceiling. The use of PCA in a Bayesian probabilistic grid allows to fuse the highly compressed PCA database information, obtained with the low computational effort, in an environment where repetitive scenarios can occur. To avoid the negative impact in the localization estimate caused by the corrupted data existing in the 2D depth sensor, an extension to the classic PCA algorithm is suggested. Thus, the proposed method allows the self-localization of mobile robots in indoor environments with bounded accuracy and working in a wide range of illumination conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

A Bayesian grid method PCA-based for mobile robots localization in unstructured environments

Rodrigues

Cardeira

Carreira

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

View full text Add to dashboard Cite

show abstract

“…a 2D image problem results. The use of ceiling vision in mobile robot navigation is successfully implemented in [9], [16], [6].…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of a visual odometry system for indoor unstructured environments

Rodrigues

Cardeira

Carreira

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

View full text Add to dashboard Cite

The main goal of this paper is to present a visual odometry system, for localization of mobile robot in indoor unstructured environments, using only ceiling depth images, captured by a Kinect sensor. The use of odometric sensors is a common practice for localization of mobile robots. The method proposed in this work exploits information from an independent source of depth data and thus allows to complement or substitute the use of classic odometric sensors, like wheels encoders, with well known limitations. The experimental validation of the proposed solution shows that the method is able to accurately compute the attitude and linear velocities that allow a more precise mobile robot localization, even in presence of corrupted data from the sensor. Furthermore, the method works in an extended range of lighting conditions, without the need to perform any specific feature extraction.

show abstract