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 - http://pointclouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.

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3D is here: Point Cloud Library (PCL)

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

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OctoMap: an efficient probabilistic 3D mapping framework based on octrees

The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).

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The Open Motion Planning Library

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environ- ment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly repre- sents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The re- sults demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

An Efficient Probabilistic 3D Mapping Framework Based on Octrees

Environment models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models: for manipulation purposes their models have to include the objects present in the world, together with their position, form, and other aspects, as well as an interpretation of these objects with respect to the robot tasks. The dissertation presented in this article (Rusu, PhD thesis, 2009) proposes Semantic 3D Object Models as a novel representation of the robot’s operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data.

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Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

This paper introduces the concept of a task motion multigraph, a data structure that can be used to reveal a difficulty specific to mobile manipulation: the possibility of planning in different state spaces in order to achieve the same goal. The different options reflect the mobile manipulator's ability to use different hardware components to perform a required task. For instance, a humanoid robot can open a door with its left arm or with its right arm. Thus, motion planning can be performed in the left arm's state space or in the right arm's state space. Given the specification of a task, it is shown how to encode the available motion planning options in a task motion multigraph. An algorithm that computes sequences of motion plans for mobile manipulators using the newly introduced notion is presented and evaluated. The algorithm makes use of information from the task motion multigraph to prioritize the spaces for which motion plans are computed. Experimental results show that reduced planning times can be obtained when considering the available planning options.

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Mobile manipulation: Encoding motion planning options using task motion multigraphs

Robots executing practical tasks in real environments are often subject to multiple constraints. These constraints include orientation constraints: e.g., keeping a glass of water upright, torque constraints: e.g., not exceeding the torque limits for an arm lifting heavy objects, visibility constraints: e.g., keeping an object in view while moving a robot arm, etc. Rejection sampling, Jacobian projection techniques and optimization-based approaches are just some of the methods that have been used to address such constraints while computing motion plans for robots performing manipulation tasks. In this work, we present an approach to handling certain types of constraints in a manner that significantly increases the efficiency of existing methods. Our approach focuses on the sampling step of a motion planner. We implement this step as the drawing of samples from a set that has been computed in advance instead of the direct sampling of constraints. We show how our approach can be applied to different constraints: orientation constraints on the end-effector of an arm, visibility constraints and dual-arm constraints. We present simulated results to validate our method, comparing it to approaches that use direct sampling of constraints.

Motion planning with constraints using configuration space approximations

We present a novel combination of motion planning techniques to compute motion plans for robotic arms. We compute plans that move the arm as close as possible to the goal region using sampling-based planning and then switch to a trajectory optimization technique for the last few centimeters necessary to reach the goal region. This combination allows fast computation and safe execution of motion plans even when the goals are very close to objects in the environment. The system incorporates realtime sensory inputs and correctly deals with occlusions that can occur when robot body parts block the sensor view of the environment. The system is tested on a 7 degree-of-freedom robot arm with sensory input from a tilting laser scanner that provides 3D information about the environment.

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Combining planning techniques for manipulation using realtime perception

Sampling-based motion planners are often used to solve very high-dimensional planning problems. Many recent algorithms use projections of the state space to estimate properties such as coverage, as it is impractical to compute and store this information in the original space. Such estimates help motion planners determine the regions of space that merit further exploration. In general, the employed projections are user-defined, and to the authors' knowledge, automatically computing them has not yet been investigated. In this work, the feasibility of offline-computed random linear projections is evaluated within the context of a state-of-the art sampling-based motion planning algorithm. For systems with moderate dimension, random linear projections seem to outperform human intuition. For more complex systems it is likely that non-linear projections would be better suited.

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On the performance of random linear projections for sampling-based motion planning

OTION planning is a key problem in robotics concerned with finding a path that satisfies a goal specification subject to constraints. In its simplest form, the solution to this problem consists of finding a path connecting two states and the only constraint is to avoid collisions. Even for this version of the motion planning problem there is no efficient solution for the general case [1]. The addition of differential constraints on robot motion or more general goal specifications make motion planning even harder. Given its complexity, most planning algorithms forego completeness and optimality for slightly weaker notions such as resolution completeness or probabilistic completeness [2] and asymptotic optimality. Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many variants have been proposed over the last 20 years, yet there is still no characterization of which algorithms are wellsuited for which classes of problems. This has motivated us to develop a benchmarking infrastructure for motion planning algorithms (see Figure 1). It consists of three main components. First, we have created an extensive benchmarking software framework that is included with the Open Motion Planning Library (OMPL), a C++ library that contains implementations of many sampling-based algorithms [3]. One can immediately compare any new planning algorithm to the 29 other planning algorithms that currently exist within OMPL. There is also much flexibility in the types of motion planning problems that can be benchmarked, as discussed in Section II-A. Second, we have defined extensible formats for storing benchmark results. The formats are fairly straightforward so that other planning libraries could easily produce compatible output. Finally, we have created an interactive, versatile visualization tool for compact presentation of collected benchmark data. The tool and underlying database facilitate the analysis of performance across benchmark problems and planners. While the three components described above emphasize generality, we have also created—as an example—a simple command line tool specifically for rigid body motion planning that takes as input a plain text description of a motion planning problem. Benchmarking sampling-based planners is non-trivial for several reasons. Since these planners rely on sampling, performance cannot be judged from a single run. Instead, benchmarks need to be run repeatedly to obtain a distribution of some

Ioan A. Sucan

Papers

Mobile manipulation: Encoding motion planning options using task motion multigraphs

Motion planning with constraints using configuration space approximations

Combining planning techniques for manipulation using realtime perception

On the performance of random linear projections for sampling-based motion planning

An Extensible Benchmarking Infrastructure for Motion Planning Algorithms