Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Performance of Histogram Descriptors for the Classification of 3D Laser Range Data in Urban EnvironmentsThe selection of suitable features and their parameters for the classification of three-dimensional laser range data is a crucial issue for high-quality results. In this paper we compare the performance of different histogram descriptors and their parameters on three urban datasets recorded with various sensorsâ€”sweeping SICK lasers, tilting SICK lasers and a Velodyne 3D laser range scanner. These descriptors are 1D, 2D, and 3D histograms capturing the distribution of normals or points around a query point. We also propose a novel histogram descriptor, which relies on the spectral values in different scales. We argue that choosing a larger support radius and a z-axis based global reference frame/axis can boost the performance of all kinds of investigated classification models significantly. The 3D histograms relying on the point distribution, normal orientations, or spectral values, turned out to be the best choice for the classification in urban environments.
Exploiting Segmentation for Robust 3D Object MatchingWhile Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter, both beam-based and ICP approaches can fail to find good solutions. In this paper, we present both an optimization method for beam-based models and a novel framework for modeling observation dependencies in beam-based models using over-segmentations. This technique enables reasoning about object extents and works well in heavy clutter. We also make available a ground-truth 3D dataset for testing algorithms in this area.
Segmenting "Simple" Objects Using RGB-DSegmenting â€œsimpleâ€ objects using low-level visual cues is an important capability for a vision system to learn in an unsupervised manner. We define a â€œsimpleâ€ object as a compact region enclosed by depth and/or contact boundary in the scene. We propose a segmentation process to extract all the â€œsimpleâ€ objects that builds on the fixation-based segmentation framework  that segments a region given a point anywhere inside it. In this work, we augment that framework with a fixation strategy to automatically select points inside the â€œsimpleâ€ objects and a postsegmentation process to select only the regions corresponding to the â€œsimpleâ€ objects in the scene. A novel characteristic of our approach is the incorporation of border ownership, the knowledge about the object side of a boundary pixel. We evaluate the process on a RGB-D dataset  and finds that it successfully extracts 91.4% of all objects in the scene.
Sparse Online Low-Rank Projection and Outlier Rejection (SOLO) for 3-D Rigid-Body Motion RegistrationMotivated by an emerging theory of robust low-rank matrix representation, in this paper, we introduce a novel solution for online rigid-body motion registration. The goal is to develop algorithmic techniques that enable a robust, real-time motion registration solution suitable for low-cost, portable 3-D camera devices. Assuming 3-D image features are tracked via a standard tracker, the algorithm first utilizes Robust PCA to initialize a low-rank shape representation of the rigid body. Robust PCA finds the global optimal solution of the initialization, while its complexity is comparable to singular value decomposition. In the online update stage, we propose a more efficient algorithm for sparse subspace projection to sequentially project new feature observations onto the shape subspace. The lightweight update stage guarantees the real-time performance of the solution while maintaining good registration even when the image sequence is contaminated by noise, gross data corruption, outlying features, and missing data. The state-of-the-art accuracy of the solution is validated through extensive simulation and a real-world experiment, while the system enjoys one to two orders of magnitude speed-up compared to well-established RANSAC solutions.
An Integrated 2D and 3D Location Measurement System Using Spiral Motion PositionerIn this paper, we describe the design and implementation of an integrated two dimensional and three dimensional location measurement system, where different types of range sensors can be mounted onto the spiral motion positioner. The proposed sensor/positioner system enables terrestrial and aerial robots to observe their surroundings in all directions without blind spots. Using a nut-and-bolt and link mechanism, the proposed positioner driven by a single stepper motor exhibits continuous three dimensional spiral trajectories over the upper hemisphere. This single axis motor driven system helps decrease the size, weight, and structural complexity of the system. Particular attention in this work is placed on how to effectively combine two dimensional and three dimensional measurement functions. We verify the validity and effectiveness of the proposed location measurement system through simulations and experiments. It is expected that the proposed system can be incorporated into a wide range of mobile robot platforms.
An Occlusion-aware Feature for Range ImagesThis paper presents a novel local feature for 3D range image data called `the line image'. It is designed to be highly viewpoint invariant by exploiting the range image to efficiently detect 3D occupancy, producing a representation of the surface, occlusions and empty spaces. We also propose a strategy for defining keypoints with stable orientations which define regions of interest in the scan for feature computation. The feature is applied to the task of object classification on sparse urban data taken with a Velodyne laser scanner, producing good results.