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Edinburgh Research Explorer Simultaneous registration of multi-view range images with adaptive kernel density estimation Simultaneous registration of multi-view range images with adaptive kernel density estimation
"... Abstract 3D surface registration can be considered one of the crucial stages of reconstructing 3D objects from depth sensor data. Aligning pairs of surfaces is a well studied problem that has resulted in fast and usually reliable algorithms addressing the task. The generalised problem of globally a ..."
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Abstract 3D surface registration can be considered one of the crucial stages of reconstructing 3D objects from depth sensor data. Aligning pairs of surfaces is a well studied problem that has resulted in fast and usually reliable algorithms addressing the task. The generalised problem of globally aligning multiple surfaces is a more complex task that has received less attention yet remains a fundamental part of extracting a model from multiple 3D surface measurements for most useful applications. In this paper, we propose a novel approach for the global registration of depth sensor data, represented by multiple dense point clouds. Point correspondences between scans and view order are unknown. Given many partial views, we estimate a kernelbased density function of the point data to determine an accurate approximation of the sampled surface. We define an energy function which implicitly considers all viewpoints simultaneously. We use this density to guide an energy minimisation in the transform space, aligning all partial views robustly. We evaluate this strategy quantitatively on synthetic and range sensor data where we find that we have competitive registration accuracy through comprehensive experiments that compare our approach with existing frameworks for this task.
2012 International Conference on Indoor Positioning and Indoor Navigation, 13–15 th November 2012 Mobile 3D Indoor Mapping Using the Continuous Normal Distributions Transform
"... Abstract—Existing approaches for indoor mapping are often either time-consuming or inaccurate. This paper presents the Continuous Normal Distributions Transform (C-NDT), an efficient approach to 3D indoor mapping that balances acquisition time, completeness and accuracy by registering scans acquired ..."
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Abstract—Existing approaches for indoor mapping are often either time-consuming or inaccurate. This paper presents the Continuous Normal Distributions Transform (C-NDT), an efficient approach to 3D indoor mapping that balances acquisition time, completeness and accuracy by registering scans acquired from a rotating LiDAR sensor mounted on a moving vehicle. C-NDT uses the robust Normal Distributions Transform (NDT) algorithm for scan registration, ensuring that the mapping is independent of the long-term quality of the odometry. We demonstrate that C-NDT produces more accurate maps than stand-alone dead-reckoning, achieves better map completeness than static scanning and is at least an order of magnitude faster than existing static scanning methods. I.
Author manuscript, published in "CISRAM'13: Cybarnetics and Intelligent Systems Robotics, Automation and Mechatronics, Manila & Pico de Loro Beach: Philippines (2013)" Point-Cloud Multi-Contact Planning for Humanoids: Preliminary Results
, 2013
"... Abstract — We present preliminary results in porting our multi-contact non-gaited motion planning framework to operate in real environments where the surroundings are acquired using an embedded camera together with a depth map sensor. We consider the robot to have no a priori knowledge of the enviro ..."
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Abstract — We present preliminary results in porting our multi-contact non-gaited motion planning framework to operate in real environments where the surroundings are acquired using an embedded camera together with a depth map sensor. We consider the robot to have no a priori knowledge of the environment, and propose a scheme to extract the information relevant for planning from an acquired point cloud. This yield the basis of an egocentric on-the-fly multi-contact planner. We then demonstrate its capacity with two simulation scenarios involving an HRP-2 robot in various environment before discussing some issues to be addressed in our quest to achieve a close loop between planning and execution in an environment explored through embedded sensors. I.
Point-Cloud Multi-Contact Planning for Humanoids: Preliminary Results
, 2013
"... HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte p ..."
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HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
