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The rationality behind Perception Processing Graphs are that most applications that deal with point cloud processing can be formulated as a concrete set of building blocks that are parameterized to achieve different results

3D is here: Point Cloud Library (PCL).

ICRA, (2011)

被引用4348|浏览463
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摘要

With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced point cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of point cloud perception: PCL (Point Cloud Library –...更多

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简介
  • For robots to work in unstructured environments, they need to be able to perceive the world.
  • The Kinect sensor for the Microsoft XBox 360 game system, based on underlying technology from PrimeSense, can be purchased for under 150, and provides real time point clouds as well as 2D images.
  • PCL is a comprehensive free, BSD licensed, library for n-D Point Clouds and 3D geometry processing.
重点内容
  • For robots to work in unstructured environments, they need to be able to perceive the world
  • Over the past 20 years, we’ve come a long way, from simple range sensors based on sonar or IR providing a few bytes of information about the world, to ubiquitous cameras to laser scanners
  • In the past few years, sensors like the Velodyne spinning LIDAR used in the DARPA Urban Challenge and the tilting laser scanner used on the PR2 have given us high-quality 3D representations of the world - point clouds
  • The rationality behind Perception Processing Graphs are that most applications that deal with point cloud processing can be formulated as a concrete set of building blocks that are parameterized to achieve different results
  • A concrete nodelet Perception Processing Graphs example for the problem of identifying a set of point clusters supported by horizontal planar areas is shown in Figure 3
结果
  • PCL is a fully templated, modern C++ library for 3D point cloud processing.
  • PCL is meant to incorporate a multitude of 3D processing algorithms that operate on point cloud data, including: filtering, feature estimation, surface reconstruction, model fitting, segmentation, registration, etc.
  • Use setInputCloud to pass the input point cloud dataset to the processing module;
  • The rationality behind PPGs are that most applications that deal with point cloud processing can be formulated as a concrete set of building blocks that are parameterized to achieve different results.
  • Based on the previous experience of designing other 3D processing libraries, and most recently, ROS, the authors decided to make each algorithm from PCL available as a standalone building block, that can be connected with other blocks, creating processing graphs, in the same way that nodes connect together in a ROS ecosystem.
  • A concrete nodelet PPG example for the problem of identifying a set of point clusters supported by horizontal planar areas is shown in Figure 3.
  • The authors present two code snippets that exhibit the flexibility and simplicity of using PCL for filtering and segmentation operations, followed by three application examples that make use of PCL for solving the perception problem: i) navigation and mapping, ii) object recognition, and iii) manipulation and grasping.
  • Algorithm 2 and Figure 7 present a code snippet and the results obtained after running it on the point cloud dataset from the left part of the figure.
  • The second example constitutes a segmentation operation for planar surfaces, using a RANSAC [7] model, as shown in Algorithm 3.
结论
  • Left: the input point cloud, right: the segmented plane represented by the inliers of the model marked with purple color.
  • An example of a more complex navigation and mapping application is shown in the left part of Figure 9, where the PR2 robot had to autonomously identify doors and their handles [8], in order to explore rooms and find power sockets [9].
  • Figure 10 presents a grasping and manipulation application [11], where objects are first segmented from horizontal planar tables, clustered into individual units, and a registration operation is applied that attaches semantic information to each cluster found.
基金
  • PCL is supported by an international community of robotics and perception researchers
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