Keypoint based autonomous registration of terrestrial laser point-clouds

Abstract:Terrestrial laser scanning is becoming a standard for 3D modeling of complex scenes. Results of the scan contain detailed geometric information about the scene; however, the lack of semantic details still constitutes a gap in ensuring this data is usable for mapping. This paper proposes a framework for recognition of objects in laser scans; aiming to utilize all the available information, range, intensity and color information integrated into the extraction framework. Instead of using the 3D point cloud, which is complex to process since it lacks an inherent neighborhood structure, we propose a polar representation which facilitates low-level image processing tasks, e.g., segmentation and texture modeling. Using attributes of each segment, a feature space analysis is used to classify segments into objects. This process is followed by a fine-tuning stage based on graph-cut algorithm, which considers the 3D nature of the data. The proposed algorithm is demonstrated on tree extraction and tested on scans containing complex objects in addition to trees. Results show a very high detection level and thereby the feasibility of the proposed framework.

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“…Cornerness is computed using the min-max operator [31]. The gradient projection in the minimal direction measures the point "cornerness" (Cn).…”

Section: Discussionmentioning

confidence: 99%

Extraction of Objects from Terrestrial Laser Scans by Integrating Geometry Image and Intensity Data with Demonstration on Trees

Barnea

Filin

2012

Remote Sensing

Self Cite

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“…Chen and Medioni (1992), Masuda and Yokoya (1994), Eggert et al (1998), Okatani and Deguchi (2002), Park and Murali (2003), Segal et al (2009) and others have proposed some variations and improvements for the ICP algorithm, using point-to-plane and plane-plane approaches. Other researchers addressed methods that integrates both depth and intensity information for the 3D registration (Godin et al, 1994;Weik, 1997;Stamos and Leordeanu, 2003;Barnea and Filin, 2008;Swadzba et al, 2007;May et al, 2009;Ye and Bruch, 2010;Khoshelham, 2010). The aforementioned approaches discussed there, make use of the depth image information supplied by a laser scanning device or range cameras, to transfer the selected 2D features to 3D points.…”

Section: Related Workmentioning

confidence: 99%

A Weighted Closed-Form Solution for RGB-D Data Registration

Vestena¹,

Santos²,

Jr.³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Existing 3D indoor mapping of RGB-D data are prominently point-based and feature-based methods. In most cases iterative closest point (ICP) and its variants are generally used for pairwise registration process. Considering that the ICP algorithm requires an relatively accurate initial transformation and high overlap a weighted closed-form solution for RGB-D data registration is proposed. In this solution, we weighted and normalized the 3D points based on the theoretical random errors and the dual-number quaternions are used to represent the 3D rigid body motion. Basically, dual-number quaternions provide a closed-form solution by minimizing a cost function. The most important advantage of the closed-form solution is that it provides the optimal transformation in one-step, it does not need to calculate good initial estimates and expressively decreases the demand for computer resources in contrast to the iterative method. Basically, first our method exploits RGB information. We employed a scale invariant feature transformation (SIFT) for extracting, detecting, and matching features. It is able to detect and describe local features that are invariant to scaling and rotation. To detect and filter outliers, we used random sample consensus (RANSAC) algorithm, jointly with an statistical dispersion called interquartile range (IQR). After, a new RGB-D loop-closure solution is implemented based on the volumetric information between pair of point clouds and the dispersion of the random errors. The loop-closure consists to recognize when the sensor revisits some region. Finally, a globally consistent map is created to minimize the registration errors via a graph-based optimization. The effectiveness of the proposed method is demonstrated with a Kinect dataset. The experimental results show that the proposed method can properly map the indoor environment with an absolute accuracy around 1.5% of the travel of a trajectory.

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“…All selected conjugate planes were used in 3-D surface minimization. The angle can be calculated using Equation (6). …”

Section: Matching Criterionmentioning

confidence: 99%

“…The registration precision indicates the geometrical difference between two systems. Barnea and Filin [6] transferred the 3-D point clouds to 2-D panoramic range images and extracted the registration key points to improve the computational time of conjugate points selection.…”

Section: Introductionmentioning

confidence: 99%

Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds

Teo

Huang

2014

Remote Sensing

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Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve geometric consistency. The registration of airborne and terrestrial mobile LiDAR is a challenging task because the point densities and scanning directions differ. We proposed a scheme for the registration of airborne and terrestrial mobile LiDAR using the least squares 3-D surface registration technique to minimize the surfaces between two datasets. To analyze the effect of point density in registration, the simulation data simulated different conditions and estimated the theoretical errors. The test data were the point clouds of the airborne LiDAR system (ALS) and the mobile LiDAR system (MLS), which were acquired by Optech ALTM 3070 and Lynx, respectively. The resulting simulation analysis indicated that the accuracy of registration improved as the density increased. For the test dataset, the registration error of mobile LiDAR between different trajectories improved from 40 cm to 4 cm, and the registration error between ALS and MLS improved from 84 cm to 4 cm. These results indicate that the proposed methods can obtain 5 cm accuracy between ALS and MLS.

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Keypoint based autonomous registration of terrestrial laser point-clouds

Cited by 102 publications

References 30 publications

Extraction of Objects from Terrestrial Laser Scans by Integrating Geometry Image and Intensity Data with Demonstration on Trees

Extraction of Objects from Terrestrial Laser Scans by Integrating Geometry Image and Intensity Data with Demonstration on Trees

A Weighted Closed-Form Solution for RGB-D Data Registration

Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds

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