The Pose Estimation of Mobile Robot Based on Improved Point Cloud Registration

Miao, Yanzi; Liu, Yang; Ma, Hongmei; Jin, Huile

doi:10.5772/62342

Cited by 9 publications

(4 citation statements)

References 22 publications

(23 reference statements)

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“…If we have the source point clouds at the origin location, the object pose can be obtained by registration method. 27 In ICP algorithm, the maximum matching threshold d max should be given to get the correspondent points. If the distance between two point clouds is large, d max should also be large.…”

Section: Registration Methods Combing Sac-ia and Icp Algorithmsmentioning

confidence: 99%

Vehicle pose estimation algorithm for parking automated guided vehicle

Zhang

Wang

Wang³

et al. 2020

International Journal of Advanced Robotic Systems

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Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out.

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Section: Registration Methods Combing Sac-ia and Icp Algorithmsmentioning

confidence: 99%

Vehicle pose estimation algorithm for parking automated guided vehicle

Zhang

Wang

Wang³

et al. 2020

International Journal of Advanced Robotic Systems

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“…A key process in the NDT algorithm is to build grids for point clouds, but grid size is difficult to determine. The use of different grid sizes to organize point clouds therefore becomes an effective way to establish grids for 3D point clouds [ 142 , 143 , 144 ].…”

Section: Registration Techniques For Lidar Datamentioning

confidence: 99%

Registration of Laser Scanning Point Clouds: A Review

Cheng

Chen

Liu

et al. 2018

Sensors

205

109

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The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

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“…Discretization brings automatic soft data association without the need of inferring GMM weights. An effective discretization strategy requires suitable voxel sizes and efficient voxel deployment, for instance a forest of octrees [19], distance based voxel sizing [46], hierarchical voxel tree deployment [47], and cell clustering [48]. Comparing to NDT, the proposed method is also data association free, but it is further a continuous representation, thus avoids the above concerns caused by discretization.…”

Section: A Mixture Of Gaussian-based Registration Frameworkmentioning

confidence: 99%

A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras

Zhang¹,

Lin²,

Lin³

et al. 2020

Preprint

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This paper reports on a novel nonparametric rigid point cloud registration framework that jointly integrates geometric and semantic measurements such as color or semantic labels into the alignment process and does not require explicit data association. The point clouds are represented as nonparametric functions in a reproducible kernel Hilbert space. The alignment problem is formulated as maximizing the inner product between two functions, essentially a sum of weighted kernels, each of which exploits the local geometric and semantic features. As a result of the continuous models, analytical gradients can be computed, and a local solution can be obtained by optimization over the rigid body transformation group. Besides, we present a new point cloud alignment metric that is intrinsic to the proposed framework and takes into account geometric and semantic information. The evaluations using publicly available stereo and RGB-D datasets show that the proposed method outperforms state-of-the-art outdoor and indoor frame-to-frame registration methods. An open-source GPU implementation is also provided.

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The Pose Estimation of Mobile Robot Based on Improved Point Cloud Registration

Cited by 9 publications

References 22 publications

Vehicle pose estimation algorithm for parking automated guided vehicle

Vehicle pose estimation algorithm for parking automated guided vehicle

Registration of Laser Scanning Point Clouds: A Review

A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras

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