Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAM

Chekhlov, Denis; Gee, Andrew P.; Calway, Andrew; Mayol-Cuevas, Walterio

doi:10.1109/ismar.2007.4538840

Cited by 77 publications

(45 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chekhlov employs RANSAC to search for planes in the point cloud of the SLAM map and the best-fit plane is determined with the inlying points from the plane hypothesis with the most consensuses [4]. The plane structural components are augmented into the SLAM state, maintaining inherent uncertainties via a full covariance re presentation [13].…”

Section: Related Workmentioning

confidence: 99%

“…The SLAM method generates a sparse cloud of point features that is suitable for estimating the pose of the camera but makes little effort to extract any geometric understanding from the map [1,4]. This paper deals with the problem of building the planar structure of a robot navigation environment and estimating the localization on a qualitative basis.…”

Section: Introductionmentioning

confidence: 99%

“…The image patch is a small region with characteristic appearances and the scene is represented with a collection of patches on the structural plane. Because structural information that included straight lines and planes is abundant in man-made environments, they are used commonly in various vision tasks [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Visual Feature and Geometric Constraints for Robot Localization

Lee¹,

Lee²,

Eem³

et al. 2015

IJCA

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Visual Feature and Geometric Constraints for Robot Localization

Lee¹,

Lee²,

Eem³

et al. 2015

IJCA

View full text Add to dashboard Cite

show abstract

“…This is feasible only for systems that need a small number of landmarks. For example, augmented reality systems (Chekhlov, Gee, Calway, & Mayol-Cuevas, 2007) that exist within a single room will see the same set of landmarks repeatedly, and, with careful selection, a relatively small set can cover the entire workspace. In comparison, FastSLAM (Montemerlo, Thrun, Koller, & Wegbreit, 2003) creates a separate Kalman filter to track each additional landmark, whereas the camera motion is estimated using a particle filter.…”

Section: Visual Slammentioning

confidence: 99%

Developing monocular visual pose estimation for arctic environments

Williams

Howard

2009

Journal of Field Robotics

View full text Add to dashboard Cite

Arctic regions present one of the harshest environments on Earth for people or mobile robots, yet many important scientific studies, particularly those involving climate change, require measurements from these areas. For the successful deployment of mobile sensors in the Arctic, a high-quality localization system is required. Although a global positioning system can provide coarse positioning (within several meters), it cannot provide any orientation information. A single-camera-pose-estimation system is presented, based on visual odometry techniques, which is capable of operating in the feature-poor environments of the Arctic. To validate the system, a prototype rover was developed and fielded on a glacier in Alaska. The resulting pose estimates compare favorably to values obtained by hand registering the same video sequence. Although pose errors do accumulate over time, these errors are typical of a standard odometry system but obtained in an environment where standard odometry is not practical. C 2009 Wiley Periodicals, Inc.

show abstract

“…Vision based SLAM is used for real applications such as augmented reality [3,6]. Most existing monocular vision based SLAM techniques employ point features as landmarks.…”

Section: Introductionmentioning

confidence: 99%

Fast Line Description for Line-based SLAM

Hirose¹,

Saitō²

2012

Procedings of the British Machine Vision Conference 2012

View full text Add to dashboard Cite

Simultaneous localization and mapping (SLAM) is a technique to simultaneously perform mapping of environments and localization of a camera in real-time. Most existing monocular vision based SLAM techniques use point features as landmarks. However, images of artificial environments with little texture often contain many line segments, whereas few point features can be localized in such a scene. We propose here a realtime line-based SLAM system, and a novel method for describing the features of line segments (LEHF:Line-based Eight-directional Histogram Feature) in order to establish correct 2D and 3D line correspondences (2D-3D correspondences). LEHF is a fast and efficient way of describing features of line segments, which are detected by the line segment detector (LSD) method. The line-based orthogonal iteration (LBOI) method and the RANSAC algorithm are applied for the camera pose estimation. We conducted an experiment in order to test our SLAM system in a desktop environment and to perform augmented reality (AR). Moreover our SLAM system was evaluated by synthetic data.

show abstract

Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAM

Cited by 77 publications

References 11 publications

Using Visual Feature and Geometric Constraints for Robot Localization

Using Visual Feature and Geometric Constraints for Robot Localization

Developing monocular visual pose estimation for arctic environments

Fast Line Description for Line-based SLAM

Contact Info

Product

Resources

About