In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features for realtime applications. To validate our results we demonstrate their efficiency for 3D registration and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment).

Fast Point Feature Histograms (FPFH) for 3D registration

In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video frame rate (30 Hz). The ground-truth trajectory was obtained from a motion-capture system with eight high-speed tracking cameras (100 Hz). The dataset consists of 39 sequences that were recorded in an office environment and an industrial hall. The dataset covers a large variety of scenes and camera motions. We provide sequences for debugging with slow motions as well as longer trajectories with and without loop closures. Most sequences were recorded from a handheld Kinect with unconstrained 6-DOF motions but we also provide sequences from a Kinect mounted on a Pioneer 3 robot that was manually navigated through a cluttered indoor environment. To stimulate the comparison of different approaches, we provide automatic evaluation tools both for the evaluation of drift of visual odometry systems and the global pose error of SLAM systems. The benchmark website [1] contains all data, detailed descriptions of the scenes, specifications of the data formats, sample code, and evaluation tools.

/pdf/a-benchmark-for-the-evaluation-of-rgb-d-slam-systems-4sau0k4128.pdf

A benchmark for the evaluation of RGB-D SLAM systems

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

We propose a real-time method for odometry and mapping using range measurements from a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation can cause mis-registration of the resulting point cloud. To date, coherent 3D maps can be built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements. The key idea in obtaining this level of performance is the division of the complex problem of simultaneous localization and mapping, which seeks to optimize a large number of variables simultaneously, by two algorithms. One algorithm performs odometry at a high frequency but low fidelity to estimate velocity of the lidar. Another algorithm runs at a frequency of an order of magnitude lower for fine matching and registration of the point cloud. Combination of the two algorithms allows the method to map in real-time. The method has been evaluated by a large set of experiments as well as on the KITTI odometry benchmark. The results indicate that the method can achieve accuracy at the level of state of the art offline batch methods.

/pdf/loam-lidar-odometry-and-mapping-in-real-time-43agyngf9i.pdf

LOAM: Lidar Odometry and Mapping in Real-time

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

6D SLAM (simultaneous localization and mapping) or 6D concurrent localization and mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y, and z coordinates and the roll, yaw, and pitch angles. Robot motion and localization on natural surfaces, e.g., driving outdoor with a mobile robot, must regard these degrees of freedom. This paper presents a robotic mapping method based on locally consistent 3D laser range scans. Iterative Closest Point scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system. A new strategy for fast data association, cached kd-tree search, leads to feasible computing times. With no ground-truth data available for outdoor environments, point relations in maps are compared to numerical relations in uncalibrated aerial images in order to assess the metric validity of the resulting 3D maps. © 2007 Wiley Periodicals, Inc.

/pdf/6d-slam-3d-mapping-outdoor-environments-289noc62xq.pdf

6D SLAM—3D mapping outdoor environments

The Hough Transform is a well-known method for detecting parameterized objects. It is the de facto standard for detecting lines and circles in 2-dimensional data sets. For 3D it has attained little attention so far. Even for the 2D case high computational costs have lead to the development of numerous variations for the Hough Transform. In this article we evaluate different variants of the Hough Transform with respect to their applicability to detect planes in 3D point clouds reliably. Apart from computational costs, the main problem is the representation of the accumulator. Usual implementations favor geometrical objects with certain parameters due to uneven sampling of the parameter space. We present a novel approach to design the accumulator focusing on achieving the same size for each cell and compare it to existing designs.

/pdf/the-3d-hough-transform-for-plane-detection-in-point-clouds-a-20uuh9opm2.pdf

The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

To create with an autonomous mobile robot a 3D volumetric map of a scene it is necessary to gage several 3D scans and to merge them into one consistent 3D model. This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. Robot motion on natural surfaces has to cope with yaw, pitch and roll angles, turning pose estimation into a problem in six mathematical dimensions. A fast variant of the Iterative Closest Points algorithm registers the 3D scans in a common coordinate system and relocalizes the robot. Finally, consistent 3D maps are generated using a global relaxation. The algorithms have been tested with 3D scans taken in the Mathies mine, Pittsburgh, PA. Abandoned mines pose significant problems to society, yet a large fraction of them lack accurate 3D maps.

/pdf/6d-slam-with-an-application-in-autonomous-mine-mapping-3hvirivmdd.pdf

6D SLAM with an application in autonomous mine mapping

http://kos.informatik.uni-osnabrueck.de/download/ras2007.pdf

Globally consistent 3D mapping with scan matching

http://kos.informatik.uni-osnabrueck.de/download/raas2005-highspeed-laser-localization.pdf

Kai Lingemann

Papers

6D SLAM—3D mapping outdoor environments

The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

6D SLAM with an application in autonomous mine mapping

Globally consistent 3D mapping with scan matching

High-speed laser localization for mobile robots