TechTalks from event: Technical session talks from ICRA 2012

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Multi Robots: Task Allocation

  • Competitive Analysis of Repeated Greedy Auction Algorithm for Online Multi-Robot Task Assignment Authors: Luo, Lingzhi; Chakraborty, Nilanjan; Sycara, Katia
    We study an online task assignment problem for multi-robot systems where robots can do multiple tasks during their mission and the tasks arrive dynamically in groups. Each robot can do at most one task from a group and the total number of tasks a robot can do is bounded by its limited battery life. There is a payoff for assigning each robot to a task and the objective is to maximize the total payoff. A special case, where each group has one task and each robot can do one task is the online maximum weighted bipartite matching problem (MWBMP). For online MWBMP, it is known that, under some assumptions on the payoffs, a greedy algorithm has a {em competitive ratio} of $frac{1}{3}$. Our key result is to prove that for the general problem, under the same assumptions on the payoff as in MWBMP and an assumption on the number of tasks arising in each group, a repeated auction algorithm, where each group of tasks is (near) optimally allocated to the available group of robots has a guaranteed competitive ratio. We also prove that (a) without the assumptions on the payoffs, it is impossible to design an algorithm with any performance guarantee and (b) without the assumption on the task profile, the algorithms that can guarantee a feasible allocation (if one exists) have arbitrarily bad performance in the worst case. Additionally, we present simulation results depicting the average case performance of the repeated greedy auction algorithm.
  • Tunable Routing Solutions for Multi-Robot Navigation Via the Assignment Problem: A 3D Representation of the Matching Graph Authors: Liu, Lantao; Shell, Dylan
    In several scenarios in which new robots and tasks are added to a network of already deployed, interchangeable robots, a trade-off arises in seeking to minimize cost to execute the tasks and the level of disruption to the system, This paper considers a navigation-oriented variant of this problem and proposes a parameterizable method to adjust the optimization criterion: from minimizing global travel time (or energy, or distance), to minimizing interruption (i.e., obtaining the fewest number of robot reassignments), and mixtures in-between. Paths are computed by task-allocation formulation in which destination locations of newly deployed robots are added as tasks to an existing allocation. We adapt the graph matching variant of the Hungarian algorithm—originally designed to solve the optimal assignment problem in complete graphs—to construct routing paths by showing that there is a three-dimensional spatial interpretation of the Hungarian bipartite graph. When new agent-task pairs are inserted, the assignment is globally reallocated in an incremental fashion so it requires only linear time when the robots’ traversal options have bounded degree. The algorithm is studied systematically in simulation and also validated with physical robots.
  • An Efficient Stochastic Clustering Auction for Heterogeneous Robot Teams Authors: Zhang, Kai; Collins, Emmanuel; Barbu, Adrian
    Stochastic Clustering Auctions (SCAs) constitute a class of cooperative auction methods that enable improvement of the global cost of the task allocations obtained with fast greedy algorithms. Prior research had developed Contracts Sequencing Algorithms (CSAs) that are deterministic and enable transfers, swaps, and other types of contracts between team members. In contrast to CSAs, SCAs use stochastic transfers or swaps between the task clusters assigned to each team member and have algorithm parameters that can enable tradeoffs between optimality and computational and communication requirements. The first SCA was based on a "Gibbs Sampler" and constrained the stochastic cluster reallocations to simple single transfers or swaps; it is applicable to heterogeneous teams. Subsequently, a more efficient SCA was developed, based on the generalized Swendsen-Wang method; it achieves the increased efficiency by connecting tasks that appear to be synergistic and then stochastically reassigning these connected tasks, hence enabling more complex and efficient movements between clusters than the first SCA. However, its application was limited to homogeneous teams. The contribution of this work is to present an efficient SCA for heterogeneous teams; it is based on a modified Swendsen-Wang method.
  • Stochastic Motion Planning with Path Constraints and Application to Optimal Agent, Resource and Route Planning Authors: Lim, Sejoon
    We present algorithms for a motion planning for multiple agents whose goals are to visit multiple locations with probabilistic guarantees for achieving the goal. Though much research has been done in stochastic shortest path algorithms, the existing algorithms focus on the single-origin single-destination problem for one agent. This paper formulates a general framework for the stochastic shortest path problem with visit node constraints designed to achieve a specific goal with multiple agents, multiple resources, and multiple destinations. The constraints are defined by a set of sequences of nodes to be visited. Given predetermined constraints, our motion planning problem consists of finding the best agents, resources, and destinations, and the path through a sequence of nodes representing them. The technique in this paper solves the problem at the same level of complexity as solving the single-origin single-destination problem by parallelization. We demonstrate the algorithm by a Web-based traffic navigation guide system and evaluate the algorithm’s performance.
  • Accounting for Uncertainty in Simultaneous Task and Motion Planning Using Task Motion Multigraphs Authors: Sucan, Ioan Alexandru; Kavraki, Lydia
    This paper describes an algorithm that considers uncertainty while solving the simultaneous task and motion planning (STAMP) problem. Information about uncertainty is transferred to the task planning level from the motion planning level using the concept of a task motion multigraph (TMM). TMMs were introduced in previous work to improve the efficiency of solving the STAMP problem for mobile manipulators. In this work, Markov Decision Processes are used in conjunction with TMMs to select sequences of actions that solve the STAMP problem such that the resulting solutions have higher probability of feasibility. Experimental evaluation indicates significantly improved probability of feasibility for solutions to the STAMP problem, compared to algorithms that ignore uncertainty information when selecting possible sequences of actions. At the same time, the efficiency due to TMMs is largely maintained.

3D Surface Models, Point Cloud Processing

  • A Semi-Local Method for Iterative Depth-Map Refinement Authors: McKinnon, David; Smith, Ryan N.; Upcroft, Ben
    Building a photorealistic, 3D model of an object or a complete scene from image-based methods is a fundamental problem in computer vision, and has many applications in robotic perception, navigation, exploration and mapping. In this paper, we extend current state-of-the-art in the computation of depth maps by presenting an accurate and computationally efficient iterative hierarchical algorithm for multi-view stereo. The algorithm is designed to utilise all available contextual information to compute highly-accurate and robust depth maps by iteratively examining different image resolutions in an image-pyramid. The novelty in our approach is that we are able to incrementally improve the depth fidelity as the algorithm progresses through the image pyramid by utilising a local method. This is achieved in a computationally efficient manner by simultaneously enforcing the consistency of the depth-map by continual comparison with neighbouring depth-maps. We present a detailed description of the algorithm, and describe how each step is carried out. The proposed technique is used to analyse multi-view stereo data from two well-known, standard datasets, and presented results show a significant decrease in computation time, as well as an increase in overall accuracy of the computed depth maps.
  • Convex Bricks: A New Primitive for Visual Hull Modeling and Reconstruction Authors: Chari, Visesh; Agrawal, Amit; Taguchi, Yuichi; Ramalingam, Srikumar
    Industrial automation tasks typically require a 3D model of the object for robotic manipulation. The ability to reconstruct the 3D model using a sample object is useful when CAD models are not available. For textureless objects, visual hull of the object obtained using silhouette-based reconstruction can avoid expensive 3D scanners for 3D modeling. We propose convex brick (CB), a new 3D primitive for modeling and reconstructing a visual hull from silhouettes. CB's are powerful in modeling arbitrary non-convex 3D shapes. Using CB, we describe an algorithm to generate a polyhedral visual hull from polygonal silhouettes; the visual hull is reconstructed as a combination of 3D convex bricks. Our approach uses well-studied geometric operations such as 2D convex decomposition and intersection of 3D convex cones using linear programming. The shape of CB can adapt to the given silhouettes, thereby significantly reducing the number of primitives required for a volumetric representation. Our framework allows easy control of reconstruction parameters such as accuracy and the number of required primitives. We present an extensive analysis of our algorithm and show visual hull reconstruction on challenging real datasets consisting of highly non-convex shapes. We also show real results on pose estimation of an industrial part in a bin-picking system using the reconstructed visual hull.
  • Real-Time Compression of Point Cloud Streams Authors: Kammerl, Julius; Blodow, Nico; Rusu, Radu Bogdan; Gedikli, Suat; Beetz, Michael; Steinbach, Eckehard
    We present a novel lossy compression approach for point cloud streams which exploits spatial and temporal redundancy within the point data. Our proposed compression framework can handle general point cloud streams of arbitrary and varying size, point order and point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the point clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive point clouds. By encoding their structural differences, we can successively extend the point clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the point cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.
  • Point Cloud Segmentation with LIDAR Reflection Intensity Behavior Authors: Tatoglu, Akin; Pochiraju, Kishore
    Light Detection and Ranging (LIDAR) scans are increasingly being used for 3D map construction and reverse engineering. The utility and benefit of the processed data maybe enhanced if the objects and geometry of the area scanned can be segmented and labeled. In this paper, we present techniques to model the intensity of the laser reflection return from a point during LIDAR scanning to determine diffuse and specular reflection properties of the scanned surface. Using several illumination models, the reflection properties of the surface are characterized by Lambertian diffuse reflection model and Phong, Gaussian and Beckmann specular models. Experimental set up with eight different surfaces with varied textures and glossiness enabled measurement of algorithm performance. Examples of point cloud segmentation with the presented approach are presented.


  • 3-D Mutual Localization with Anonymous Bearing Measurements Authors: Cognetti, Marco; Stegagno, Paolo; Franchi, Antonio; Oriolo, Giuseppe; Buelthoff, Heinrich H.
    We present a decentralized algorithm for estimating mutual 3-D poses in a group of mobile robots, such as a team of UAVs. Our algorithm uses bearing measurements reconstructed, e.g., by a visual sensor, and inertial measurements coming from the robot IMU. Since identification of a specific robot in a group would require visual tagging and may be cumbersome in practice, we simply assume that the bearing measurements are anonymous. The proposed localization method is a non-trivial extension of our previous algorithm for the 2-D case, and exhibits similar performance and robustness. An experimental validation of the algorithm has been performed using quadrotor UAVs.
  • Online Model Estimation of Ultra-Wideband TDOA Measurements for Mobile Robot Localization Authors: Prorok, Amanda; Gonon, Lukas; Martinoli, Alcherio
    Ultra-wideband (UWB) localization is a recent technology that promises to outperform many indoor localization methods currently available. Yet, non-line-of-sight (NLOS) positioning scenarios can create large biases in the time-difference-of-arrival (TDOA) measurements, and must be addressed with accurate measurement models in order to avoid significant localization errors. In this work, we first develop an efficient, closed-form TDOA error model and analyze its estimation characteristics by calculating the Cramer-Rao lower bound (CRLB). We subsequently detail how an online Expectation Maximization (EM) algorithm is adopted to find an elegant formalism for the maximum likelihood estimate of the model parameters. We perform real experiments on a mobile robot equipped with an UWB emitter, and show that the online estimation algorithm leads to excellent localization performance due to its ability to adapt to the varying NLOS path conditions over time.
  • Orientation Only Loop-Closing with Closed-Form Trajectory Bending Authors: Dubbelman, Gijs; Browning, Brett; Hansen, Peter; Dias, M. Bernardine
    In earlier work closed-form trajectory bending was shown to provide an efficient and accurate out-of-core solution for loop-closing exactly sparse trajectories. Here we extend it to fuse exactly sparse trajectories, obtained from relative pose estimates, with absolute orientation data. This allows us to close-the-loop using absolute orientation data only. The benefit is that our approach does not rely on the observations from which the trajectory was estimated nor on the probabilistic links between poses in the trajectory. It therefore is highly efficient. The proposed method is compared against regular fusion and an iterative trajectory bending solution using a 5 km long urban trajectory. Proofs concerning optimality of our method are provided.
  • Capping Computation Time and Storage Requirements for Appearance-Based Localization with CAT− SLAM Authors: Maddern, William; Milford, Michael J; Wyeth, Gordon
    Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on long-term autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.
  • Improving the Accuracy of EKF-Based Visual-Inertial Odometry Authors: Li, Mingyang; Mourikis, Anastasios
    In this paper, we perform a rigorous analysis of EKF-based visual-inertial odometry (VIO) and present a method for improving its performance. Specifically, we examine the properties of EKF-based VIO, and show that the standard way of computing Jacobians in the filter inevitably causes inconsistency and loss of accuracy. This result is derived based on an observability analysis of the EKF's linearized system model, which proves that the yaw erroneously appears to be observable. In order to address this problem, we propose modifications to the multi-state constraint Kalman filter (MSCKF) algorithm, which ensure the correct observability properties without incurring additional computational cost. Extensive simulation tests and real-world experiments demonstrate that the modified MSCKF algorithm outperforms competing methods, both in terms of consistency and accuracy.