TechTalks from event: Technical session talks from ICRA 2012

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Range Imaging

  • Performance of Histogram Descriptors for the Classification of 3D Laser Range Data in Urban Environments Authors: Behley, Jens; Steinhage, Volker; Cremers, Armin
    The 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 sensors—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 Matching Authors: Krainin, Michael; Konolige, Kurt; Fox, Dieter
    While 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-D Authors: Mishra, Ajay; Shrivastava, Ashish; Aloimonos, Yiannis
    Segmenting “simple” objects using low-level visual cues is an important capability for a vision system to learn in an unsupervised manner. We define a “simple” object as a compact region enclosed by depth and/or contact boundary in the scene. We propose a segmentation process to extract all the “simple” objects that builds on the fixation-based segmentation framework [13] 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 “simple” objects and a postsegmentation process to select only the regions corresponding to the “simple” 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 [9] 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 Registration Authors: Yang, Allen; Slaughter, Chris; Bagwell, Justin; Checkles, Costa; Sentis, Luis; Vishwanath, Sriram
    Motivated 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 Positioner Authors: Lee, Geunho; Noguchi, Naoto; Kawasaki, Nobuya; Chong, Nak Young
    In 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 Images Authors: Quadros, Alastair James; Underwood, James Patrick; Douillard, Bertrand
    This 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.

Collision

  • Time-Optimal Multi-Stage Motion Planning with Guaranteed Collision Avoidance Via an Open-Loop Game Formulation Authors: Takei, Ryo; Huang, Haomiao; Ding, Jerry; Tomlin, Claire
    We present an efficient algorithm which computes, for a kinematic point mass moving in the plane, a time-optimal path that visits a sequence of target sets while conservatively avoiding collision with moving obstacles, also modelled as kine- matic point masses, but whose trajectories are unknown. The problem is formulated as a pursuit-evasion differential game, and the underlying construction is based on optimal control. The algorithm, which is a variant of the fast marching method for shortest path problems, can handle general dynamical constraints on the players and arbitrary domain geometry (e.g. obstacles, non-polygonal boundaries). Applications to a two- stage game, capture-the-flag, is presented.
  • Execution and Analysis of High-Level Tasks with Dynamic Obstacle Anticipation Authors: Johnson, Benjamin; Havlak, Frank; Campbell, Mark; Kress-Gazit, Hadas
    This paper uniquely embeds high-level robot controllers with sensor data obtained from abstracting probabilistic anticipation of the behavior of dynamic obstacles. An example problem of an autonomous vehicle operating in an urban environment, in the presence of other vehicles and pedestrians, is used as motivation. The correct-by-construction controller is automatically synthesized from a set of high-level tasks, specified as temporal logic formulas. The anticipated behavior of other vehicles is abstracted to a set of propositions describing the safety of road segments at intersections, and used as the output of high-level sensors for the controller. Such an input to the controller is inherently probabilistic, and this paper investigates the types of probabilistic guarantees that can be made about the system using both formal and statistical analysis.
  • A Depth Space Approach to Human-Robot Collision Avoidance Authors: Flacco, Fabrizio; Kroeger, Torsten; De Luca, Alessandro; Khatib, Oussama
    In this paper a real-time collision avoidance approach is presented for safe human-robot coexistence. The main contribution is a fast method to evaluate distances between the robot and possibly moving obstacles (including humans), based on the concept of depth space. With these distances, repulsive vectors are generated that are used to control the robot while executing a generic motion task. The repulsive vectors can also take advantage of an estimation of the obstacle velocity. In order to preserve the execution of a Cartesian task with a redundant manipulator, a simple collision avoidance algorithm has been implemented where different reaction behaviors are set up for the end-effector and for other control points along the robot structure. The complete collision avoidance framework, from perception of the environment to joint-level robot control, is presented for a 7-dof KUKA Light-Weight-Robot IV using the Microsoft Kinect sensor. Experimental results are reported for dynamic environments with obstacles and a human.
  • LQG-Obstacles: Feedback Control with Collision Avoidance for Mobile Robots with Motion and Sensing Uncertainty Authors: van den Berg, Jur; Wilkie, David; Guy, Stephen J.; Niethammer, Marc; Manocha, Dinesh
    This paper presents LQG-Obstacles, a new concept that combines linear-quadratic feedback control of mobile robots with guaranteed avoidance of collisions with obstacles. Our approach generalizes the concept of Velocity Obstacles to any robotic system with a linear Gaussian dynamics model. We integrate a Kalman filter for state estimation and an LQR feedback controller into a closed-loop dynamics model of which a higher-level control objective is the ``control input''. We then define the LQG-Obstacle as the set of control objectives that result in a collision with high probability. Selecting a control objective outside the LQG-Obstacle then produces collision-free motion. We demonstrate the potential of LQG-Obstacles by safely and smoothly navigating a simulated quadrotor helicopter with complex non-linear dynamics and motion and sensing uncertainty through three-dimensional environments with obstacles and narrow passages.
  • K-IOS: Intersection of Spheres for Efficient Proximity Query Authors: Zhang, Xinyu; Kim, Young J.
    We present a new bounding volume structure, k-IOS that is an intersection of k spheres, for accelerating proximity query including collision detection and Euclidean distance computation between arbitrary polygon-soup models that undergo rigid motion. Our new bounding volume is easy to implement and highly efficient both for its construction and runtime query. In our experiments, we have observed up to 4.0 times performance improvement of proximity query compared to an existing well-known algorithm based on swept sphere volume (SSV) [1]. Moreover, k-IOS is strictly convex that can guarantee a continuous gradient of distance function with respect to object’s configuration parameter.
  • Reciprocal Collision Avoidance for Multiple Car-Like Robots Authors: Alonso-Mora, Javier; Breitenmoser, Andreas; Beardsley, Paul; Siegwart, Roland
    In this paper a method for distributed reciprocal collision avoidance among multiple non-holonomic robots with bike kinematics is presented. The proposed algorithm, bicycle reciprocal collision avoidance (B-ORCA), builds on the concept of optimal reciprocal collision avoidance (ORCA) for holonomic robots but furthermore guarantees collision-free motions under the kinematic constraints of car-like vehicles. The underlying principle of the B-ORCA algorithm applies more generally to other kinematic models, as it combines velocity obstacles with generic tracking control. The theoretical results on collision avoidance are validated by several simulation experiments between multiple car-like robots.

Localization and Mapping

  • Point Set Registration through Minimization of the L2 Distance between 3D-NDT Models Authors: Stoyanov, Todor; Magnusson, Martin; Lilienthal, Achim, J.
    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.
  • Consistency Analysis for Sliding-Window Visual Odometry Authors: Dong-Si, Tue-Cuong; Mourikis, Anastasios
    In this paper we focus on the problem of {em visual odometry}, i.e., the task of tracking the pose of a moving platform using visual measurements. In recent years, several VO algorithms have been proposed that employ nonlinear minimization in a sliding window of poses for this task. Through the use of iterative re-linearization, these methods are capable of successfully addressing the nonlinearity of the measurement models, and have become the de-facto standard for high-precision VO. In this work, we conduct an analysis of the properties of marginalization, which is the process through which older states are removed from the sliding window. This analysis shows that the standard way of marginalizing older poses results in an erroneous change in the rank of the measurements' information matrix, and leads to underestimation of the uncertainty of the state estimates. Based on the analytical results, we also propose a simple modification of the way in which the measurement Jacobians are computed. This modification avoids the above problem, and results in an algorithm with superior accuracy, as demonstrated in both simulation tests and real-world experiments.
  • Efficient Visual Odometry Using a Structure-Driven Temporal Map Authors: Martinez-Carranza, Jose; Calway, Andrew
    We describe a method for visual odometry using a single camera based on an EKF framework. Previous work has shown that filtering based approaches can achieve accuracy performance comparable to that of optimisation methods providing that large numbers of features are used. However, computational requirements are signicantly increased and frame rates are low. We address this by employing higher level structure - in the form of planes - to efficiently parameterise features and so reduce the filter state size and computational load. Moreover, we extend a 1-point RANSAC outlier rejection method to the case of features lying on planes. Results of experiments with both simulated and real-world data demonstrate that the method is effective, achieving comparable accuracy whilst running at significantly higher frame rates.
  • Using Depth in Visual Simultaneous Localisation and Mapping Authors: Scherer, Sebastian Andreas; Dube, Daniel; Zell, Andreas
    We present a method of utilizing depth information as provided by RGBD sensors for robust real-time visual simultaneous localisation and mapping (SLAM) by augmenting monocular visual SLAM to take into account depth data. This is implemented based on the freely available software “Parallel Tracking and Mapping” (PTAM) by Georg Klein, which was originally developed for augmented reality applications. Our modifications allow PTAM to be used as a 6D visual SLAM system even without any additional information about odometry or from an inertial measurement unit.
  • A Visual Marker for Precise Pose Estimation Based on Lenticular Lenses Authors: Tanaka, Hideyuki; Sumi, Yasushi; Matsumoto, Yoshio
    Visual marker is a useful assistive tool for service robots. Existing planar visual markers have poor accuracy and stability in pose estimation, especially in frontal direction. In this study, we developed a novel visual marker based on a new principle enabling accurate and stable pose estimation even by observation from frontal direction. The marker has moire patterns which consist of lenticular lens and stripe pattern, which vary their appearance according to visual-line angle of observation. We can extract pose information from the pattern by a single camera. We developed a prototype of the marker and an algorithm for pose estimation, and then demonstrated its superiority to existing markers by some validation tests.
  • Robot Semantic Mapping through Wearable Sensor-Based Human Activity Recognition Authors: Sheng, Weihua; Li, Gang; Zhu, Chun; Du, Jianhao; Cheng, Qi
    Semantic information can help both humans and robots to understand their environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a semantic mapping approach through human activity recognition in an indoor human-robot coexisting environment. An intelligent mobile robot platform can create a 2D metric map, while human activity can be recognized using motion data from wearable motion sensors mounted on a human subject. Combined with pre-learned models of activity-to-furniture type association and robot pose estimates, the robot can determine the distribution of the furniture types on the 2D metric map. Simulations and real world experiments demonstrate that the proposed method is able to create a reliable metric map with accurate semantic information.