Using virtual scans for improved mapping and evaluation

Lakaemper, Rolf; Adluru, Nagesh

doi:10.1007/s10514-009-9149-4

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…Finally, the 'Image Homography' method uses our initial guess computed in Eq. (4) to transform the point cloud before beginning ICP. Our 'Image Homography' transformation was computed between the reference * images in the stereo camera.…”

Section: Resultsmentioning

confidence: 99%

An approach to stereo-point cloud registration using image homographies

Fox

Lyons

2012

SPIE Proceedings

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A mobile robot equipped with a stereo camera can measure both the video image of a scene and the visual disparity in the scene. The disparity image can be used to generate a collection of points, each representing the location of a surface in the visual scene as a 3D point with respect to the location of the stereo camera: a point cloud. If the stereo camera is moving, e.g., mounted on a moving robot, aligning these scans becomes a difficult, and computationally expensive problem. Many finely tuned versions of the iterative closest point algorithm (ICP) have been used throughout robotics for registration of these sets of scans. However, ICP relies on theoretical convergence to the nearest local minimum of the dynamical system: there is no guarantee that ICP will accurately align the scans. In order to address two problems with ICP, convergence time and accuracy of convergence, we have developed an improvement by using salient keypoints from successive video images to calculate an affine transformation estimate of the camera location. This transformation, when applied to the target point cloud, provides ICP an initial guess to reduce the computational time required for point cloud registration and improve the quality of registration. We report ICP convergence times with and without image information for a set of stereo data point clouds to demonstrate the effectiveness of the approach.

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Section: Resultsmentioning

confidence: 99%

An approach to stereo-point cloud registration using image homographies

Fox

Lyons

2012

SPIE Proceedings

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“…The wall extraction procedure's usage of the prior produced by the entropy compass is reminiscent of the virtual scans technique developed by Lakaemper [9]. The method presented here differs in that the prior is used only to suggest orientations of points rather than specific geometry.…”

Section: Related Workmentioning

confidence: 99%

Rapid multi-robot exploration with topometric maps

Cowley

Taylor

Southall

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-Multi-robot map building has advanced to the point where high quality occupancy grid data may be collected by multiple robots collaborating with only intermittent connectivity. However, the tasking of these agents to most efficiently build the map is a problem that has seen less attention. Unfamiliar, highly cluttered environments can confound exploration strategies that rely solely on occupancy grid frontier identification or even semantic classification methods keyed on geometric features. To reason about partial maps of novel, highly cluttered locations, hypotheses about significant structure in the disposition of free space may be used to guide exploration task assignment. A parsing of map data into places with semantic significance to the exploration task provides a foundation from which one may infer an efficient exploration strategy.

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“…<chenhy3, soerensch>@shanghaitech.edu.cn Another category of evaluation algorithms is not using ground truth paths but the maps created by the mapping system for evaluation. Image similarity methods [7] and pixellevel feature detectors [8], [9] can be adopted to evaluate the quality of maps created by their algorithms. However, these methods have their own limitations because maps often have errors like structures appearing more than once due to localization errors.…”

Section: Introductionmentioning

confidence: 99%

Advanced mapping robot and high-resolution dataset

Chen

Yang

Zhao

et al. 2020

Robotics and Autonomous Systems

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This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. Nine high-resolution cameras and two 32-beam 3D Lidars were used along with a professional, static 3D scanner for ground truth map collection. With all the sensors calibrated on the mapping robot, three datasets are collected to evaluate the performance of mapping algorithms within a room and between rooms. Based on these datasets we generate maps and trajectory data, which is then fed into evaluation algorithms. We provide the datasets for download and the mapping and evaluation procedures are made in a very easily reproducible manner for maximum comparability. We have also conducted a survey on available robotics-related datasets and compiled a big table with those datasets and a number of properties of them.

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Using virtual scans for improved mapping and evaluation

Cited by 10 publications

References 29 publications

An approach to stereo-point cloud registration using image homographies

An approach to stereo-point cloud registration using image homographies

Rapid multi-robot exploration with topometric maps

Advanced mapping robot and high-resolution dataset

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