Fast exact shortest distance queries for massive point clouds

This paper presents a generic method for generating joint trajectories for robotic manipulators with collision avoidance capability. The coordinate motion control system of the heavy-duty hydraulic manipulator resolves joint references so that a goal pose can be reached in real-time without any collisions. The control system checks whether any part of the manipulator is at risk of colliding with itself, with other manipulators, or with environmental obstacles. If there is a risk of collision, then the collision server searches the points where the collision is about to occur and calculates the shortest distance between the colliding objects. The collision server retains static and dynamic point clouds, and it uses point cloud data to calculate the shortest distance between the colliding objects. The point clouds on the server are kept up to date with the manipulators’ joint sensors and an external surveillance system. During coordinated motion control, the joint trajectories of the hydraulic manipulator are modified so that collisions can be avoided, while at the same time, the trajectory of the end-effector maintains its initial trajectory if possible. Results are given for a seven degrees of freedom redundant hydraulic manipulator to demonstrate the capability of this collision avoidance control system.

Download Full-text

Service-Oriented Processing and Analysis of Massive Point Clouds in Geoinformation Management

Lecture Notes in Geoinformation and Cartography - Service-Oriented Mapping ◽

10.1007/978-3-319-72434-8_2 ◽

2018 ◽

pp. 43-61 ◽

Cited By ~ 1

Author(s):

Sören Discher ◽

Rico Richter ◽

Matthias Trapp ◽

Jürgen Döllner

Keyword(s):

Point Clouds ◽

Massive Point ◽

Service Oriented

Download Full-text

In memory processing of massive point clouds for multi-core systems

Proceedings of the 12th International Workshop on Data Management on New Hardware - DaMoN '16 ◽

10.1145/2933349.2933356 ◽

2016 ◽

Cited By ~ 2

Author(s):

Kostis Kyzirakos ◽

Foteini Alvanaki ◽

Martin Kersten

Keyword(s):

Point Clouds ◽

Memory Processing ◽

Massive Point

Download Full-text

Error-aware construction and rendering of multi-scan panoramas from massive point clouds

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2016.09.011 ◽

2017 ◽

Vol 157 ◽

pp. 43-54 ◽

Cited By ~ 4

Author(s):

Marc Comino ◽

Carlos Andújar ◽

Antonio Chica ◽

Pere Brunet

Keyword(s):

Point Clouds ◽

Massive Point

Download Full-text

Stereo Model Selection and Point Cloud Filtering using an Out-of-Core Octree

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-373-2014 ◽

2014 ◽

Vol XL-3 ◽

pp. 373-380 ◽

Cited By ~ 1

Author(s):

K. Wenzel ◽

N. Haala ◽

D. Fritsch

Keyword(s):

Model Selection ◽

Image Matching ◽

Stereo Matching ◽

Point Clouds ◽

Outlier Rejection ◽

Object Space ◽

Massive Point ◽

3D Data ◽

Dense Image ◽

Stereo Triangulation

Dense image matching methods enable the retrieval of dense surface information using any kind of imagery. The best quality can be achieved for highly overlapping datasets, which avoids occlusions and provides highly redundant observations. Thus, images are acquired close to each other. This leads to datasets with increasing size – especially when large scenes are captured. While image acquisition can be performed in relatively short time, more time is required for data processing due to the computational complexity of the involved algorithms. For the dense surface reconstruction task, Multi-View Stereo algorithms can be used – which are typically beneficial due to the efficiency of image matching on stereo models. Our dense image matching solution SURE uses such an approach, where the result of stereo matching is fused using a multi-stereo triangulation in order to exploit the available redundancy. One key challenge of such Multi-View Stereo methods is the selection of suitable stereo models, where object space information should be considered to avoid unnecessary processing. Subsequently, the dense image matching step provides up to one 3D point for each pixel, which leads to massive point clouds. This large amount of 3D data needs to be filtered and integrated efficiently in object space. Within this paper, we present an out-of-core octree, which enables neighborhood and overlap analysis between point clouds. It is used on low-resolution point clouds to support the stereo model selection. Also, this tree is designed for the processing of massive point clouds with low memory requirements and thus can be used to perform outlier rejection, redundancy removal and resampling.

Download Full-text