Complex and photo-realistic scene representation based on range planar segmentation and model fusion

Yao, Jian; Taddei, Pierluigi; Ruggeri, Mauro R.; Vitor, Sequeira

doi:10.1177/0278364911410754

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Other methods, for instance the one introduced in [134], use statistical analysis for removing neighborhood points that are more than a fixed number of standard deviations away from the median. Similarly in [170] another statistical method is proposed to identify and remove outliers by checking for big residuals during plane fitting. When dealing with static environments either data fusion over time [96,132], or outlier removal using octree raycasting as proposed in [12] can also be used.…”

Section: Outlier Pointsmentioning

confidence: 99%

Curved Surface Patches for Rough Terrain Perception

Kanoulas¹

2016

Preprint

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Section: Outlier Pointsmentioning

confidence: 99%

Curved Surface Patches for Rough Terrain Perception

Kanoulas¹

2016

Preprint

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“…We refer the reader to more advanced research on the perception, segmentation and understanding of 3-D point clouds such as [8], [9] which result in more complete and error-free models of the environment.…”

Section: B Map Extractionmentioning

confidence: 99%

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

Fallon

Johannsson

Leonard

2012

2012 IEEE International Conference on Robotics and Automation

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This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing.

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“…The adjacent point distance [42] is defined by exploiting the grid structure of range image I related to the acquired point cloud. Let p be a valid point associated with a pixel of I, its adjacent point distance A d (p) is defined as the median of the distances between p and its adjacent valid points p k in a 3 × 3 neighbourhood of p, i.e., A d (p) = median k ||p − p k ||.…”

Section: Probabilistic Samplingmentioning

confidence: 99%

Robust surface registration using N-points approximate congruent sets

Yao

Ruggeri

Taddei

et al. 2011

EURASIP J. Adv. Signal Process.

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Scans acquired by 3D sensors are typically represented in a local coordinate system. When multiple scans, taken from different locations, represent the same scene these must be registered to a common reference frame. We propose a fast and robust registration approach to automatically align two scans by finding two sets of N-points, that are approximately congruent under rigid transformation and leading to a good estimate of the transformation between their corresponding point clouds. Given two scans, our algorithm randomly searches for the best sets of congruent groups of points using a RANSAC-based approach. To successfully and reliably align two scans when there is only a small overlap, we improve the basic RANSAC random selection step by employing a weight function that approximates the probability of each pair of points in one scan to match one pair in the other. The search time to find pairs of congruent sets of N-points is greatly reduced by employing a fast search codebook based on both binary and multi-dimensional lookup tables. Moreover, we introduce a novel indicator of the overlapping region quality which is used to verify the estimated rigid transformation and to improve the alignment robustness. Our framework is general enough to incorporate and efficiently combine different point descriptors derived from geometric and texture-based feature points or scene geometrical characteristics. We also present a method to improve the matching effectiveness of texture feature descriptors by extracting them from an atlas of rectified images recovered from the scan reflectance image. Our algorithm is robust with respect to different sampling densities and also resilient to noise and outliers. We demonstrate its robustness and efficiency on several challenging scan datasets with varying degree of noise, outliers, extent of overlap, acquired from indoor and outdoor scenarios.

show abstract

Complex and photo-realistic scene representation based on range planar segmentation and model fusion

Cited by 8 publications

References 32 publications

Curved Surface Patches for Rough Terrain Perception

Curved Surface Patches for Rough Terrain Perception

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

Robust surface registration using N-points approximate congruent sets

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