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.

/pdf/octomap-an-efficient-probabilistic-3d-mapping-framework-3dp42nnofi.pdf

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

With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.

Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

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

The Leap Motion Controller is a new device for hand gesture controlled user interfaces with declared sub-millimeter accuracy However, up to this point its capabilities in real environments have not been analyzed Therefore, this paper presents a first study of a Leap Motion Controller The main focus of attention is on the evaluation of the accuracy and repeatability For an appropriate evaluation, a novel experimental setup was developed making use of an industrial robot with a reference pen allowing a position accuracy of 02 mm Thereby, a deviation between a desired 3D position and the average measured positions below 02mmhas been obtained for static setups and of 12mmfor dynamic setups Using the conclusion of this analysis can improve the development of applications for the Leap Motion controller in the field of Human-Computer Interaction

Analysis of the Accuracy and Robustness of the Leap Motion Controller

Learning Deconvolution Network for Semantic Segmentation

Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align th ...

http://journals.sagepub.com/doi/pdf/10.1177/0278364912460895

Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations

In order to enable long-term operation of autonomous vehicles in industrial environments numerous challenges need to be addressed. A basic requirement for many applications is the creation and maintenance of consistent 3D world models. This article proposes a novel 3D spatial representation for online real-world mapping, building upon two known representations: normal distributions transform (NDT) maps and occupancy grid maps. The proposed normal distributions transform occupancy map (NDT-OM) combines the advantages of both representations; compactness of NDT maps and robustness of occupancy maps. One key contribution in this article is that we formulate an exact recursive updates for NDT-OMs. We show that the recursive update equations provide natural support for multi-resolution maps. Next, we describe a modification of the recursive update equations that allows adaptation in dynamic environments. As a second key contribution we introduce NDT-OMs and formulate the occupancy update equations that allow to build consistent maps in dynamic environments. The update of the occupancy values are based on an efficient probabilistic sensor model that is specially formulated for NDT-OMs. In several experiments with a total of 17 hours of data from a milk factory we demonstrate that NDT-OMs enable real-time performance in large-scale, long-term industrial setups.

3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments

Point set registration-the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three-Dimensional Normal Distributions Transforms. 3D-NDT models — a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3D-NDT representations of the input point sets and then formulates an objective function based on the L 2 distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms.

Point set registration through minimization of the L 2 distance between 3D-NDT models

Industrial applications often impose hard requirements on the precision of autonomous vehicle systems. As a consequence industrial Automatically Guided Vehicle (AGV) systems still use high-cost infrastructure based positioning solutions. In this paper we propose a map based localization method that fulfills the requirements on precision and repeatability, typical for industrial application scenarios. The proposed method - Normal Distributions Transform Monte Carlo Localization (NDT-MCL) is based on a well established probabilistic framework. In a novel contribution, we formulate the MCL localization approach using the Normal Distributions Transform (NDT) as an underlying representation for both map and sensor data. By relaxing the hard discretization assumption imposed by grid-map models and utilizing the piece-wise continuous NDT representation the proposed algorithm achieves substantially improved accuracy and repeatability. The proposed NDT-MCL algorithm is evaluated using offline data sets from both a laboratory and a real-world industrial environments. Additionally, we report a comparison of the proposed algorithm to grid-based MCL and to a commercial localization system when used in a closed-loop with the control system of an AGV platform. In all tests the proposed algorithm is demonstrated to provide performance superior to that of standard grid-based MCL and comparable to the performance of the commercial infrastructure based positioning system.

Normal distributions transform Monte-Carlo localization (NDT-MCL)

Effective robotic simulation depends on accurate modeling of physics and the environment as well as the robot, itself. This paper describes validation studies examining feature extraction, WaveLan radio performance, and human interaction for the USARSim robotic simulation. All four feature extraction algorithms showed strong correspondences between data collected in simulation and from real robots. In each case data extracted from a well lit scene produced a closer match to data extracted from a simulated image than to camera data from a poorly lit scene. The radio simulation also performed well in validation showing levels of attenuation due to intervening walls that were similar to signal strengths measured in the modeled environment. The human-robot interaction experiments showed close correspondence between simulator and robots in performance affected by robot model, control mode and task difficulty.

/pdf/quantitative-assessments-of-usarsim-accuracy-1rg9xhas9i.pdf

Todor Stoyanov

Papers

Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations

3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments

Point set registration through minimization of the L 2 distance between 3D-NDT models

Normal distributions transform Monte-Carlo localization (NDT-MCL)

Quantitative Assessments of USARSim Accuracy