TechTalks from event: Technical session talks from ICRA 2012

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Mapping

  • Decomposable Bundle Adjustment Using a Junction Tree Authors: Pinies, Pedro; Paz, Lina María; Heyden, Anders; Haner, Sebastian
    The Sparse Bundle Adjustment (SBA) algorithm is a widely used method to solve multi-view reconstruction problems in vision. The critical cost of SBA depends on the fill in of the reduced camera matrix whose pattern is known as the Secondary structure of the problem. In centered object applications where a large number of images are taken in a small area the camera matrix obtained when points are eliminated is dense. On the contrary, visual mapping systems where long trajectories are traversed yield sparse matrices. In this paper, we propose a Decomposable Bundle Adjustment (DBA) method which naturally adapts to the fill in pattern of the camera matrix improving the performance on visual mapping systems. The proposed algorithm is able to decompose the normal equations into small subsystems which are ordered in a junction tree structure. To solve the original system, local factorizations of the small dense matrices are passed between clusters in the tree. The DBA algorithm has been tested for simulated and real data experiments for different environment configurations showing good performance.
  • An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation Authors: Rosen, David; Kaess, Michael; Leonard, John
    Many online inference problems in computer vision and robotics are characterized by probability distributions whose factor graph representations are sparse and whose factors are all Gaussian functions of error residuals. Under these conditions, maximum likelihood estimation corresponds to solving a sequence of sparse least-squares minimization problems in which additional summands are added to the objective function over time. In this paper we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg trust-region method suitable for use in online sparse least-squares minimization. As a trust-region method, Powell's Dog-Leg enjoys excellent global convergence properties, and is known to be considerably faster than both Gauss-Newton and Levenberg-Marquardt when applied to sparse least-squares problems. Consequently, RISE maintains the speed of current state-of-the-art incremental sparse least-squares methods while providing superior robustness to objective function nonlinearities.
  • Weak Constraints Network Optimiser Authors: Berger, Cyrille
    We present a general framework to estimate the parameters of both a robot and landmarks in 3D. It relies on the use of a stochastic gradient descent method for the optimisation of the nodes in a graph of weak constraints where the landmarks and robot poses are the nodes. Then a belief propagation method combined with covariance intersection is used to estimate the uncertainties of the nodes. The first part of the article describes what is needed to define a constraint and a node models, how those models are used to update the parameters and the uncertainties of the nodes. The second part present the models used for robot poses and interest points, as well as simulation results.
  • Multi-Agent Deterministic Graph Mapping Via Robot Rendezvous Authors: Gong, Chaohui; Tully, Stephen; Kantor, George; Choset, Howie
    In this paper, we present a novel algorithm for deterministically mapping an undirected graph-like world with multiple synchronized agents. The application of this algorithm is the collective mapping of an indoor environment with multiple mobile robots while leveraging an embedded topological decomposition of the environment. Our algorithm relies on a group of agents that all depart from the same initial vertex in the graph and spread out to explore the graph. A centralized tree of graph hypotheses is maintained to consider loop-closure, which is deterministically verified when agents observe each other at a common vertex. To achieve efficient mapping, we introduce an active exploration method in which agents dynamically request rendezvous tasks from other available agents to validate graph hypotheses.

SLAM I

  • On the Number of Local Minima to the Point Feature Based SLAM Problem Authors: Huang, Shoudong; Wang, Heng; Frese, Udo; Dissanayake, Gamini
    Map joining is an efficient strategy for solving feature based SLAM problems. This paper demonstrates that joining of two 2D local maps, formulated as a nonlinear least squares problem has at most two local minima, when the associated uncertainties can be described using spherical covariance matrices. Necessary and sufficient condition for the existence of two minima is derived and it is shown that more than one minimum exists only when the quality of the local maps used for map joining is extremely poor. The analysis explains to some extent why a number of optimization based SLAM algorithms proposed in the recent literature that rely on local search strategies are successful in converging to the globally optimal solution from poor initial conditions, particularly when covariance matrices are spherical. It also demonstrates that the map joining problem has special properties that may be exploited to reliably obtain globally optimal solutions to the SLAM problem.
  • On the Comparison of Uncertainty Criteria for Active SLAM Authors: Carrillo, Henry; Reid, Ian; Castellanos, Jose A.
    In this paper, we consider the computation of the D-optimality criterion as a metric for the uncertainty of a SLAM system. Properties regarding the use of this uncertainty criterion in the active SLAM context are highlighted, and comparisons against the A-optimality criterion and entropy are presented. This paper shows that contrary to what has been previously reported, the D-optimality criterion is indeed capable of giving fruitful information as a metric for the uncertainty of a robot performing SLAM. Finally, through various experiments with simulated and real robots, we support our claims and show that the use of D-opt has desirable effects in various SLAM related tasks such as active mapping and exploration.
  • Continuous-Time Batch Estimation Using Temporal Basis Functions Authors: Furgale, Paul Timothy; Barfoot, Timothy; Sibley, Gabe
    Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable. However, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping (SLAM) problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full maximum likelihood estimation (MLE) problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian, and show how coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms using different assumptions. Results from a self-calibration experiment involving a camera and a high-rate IMU are provided to validate the approach.
  • SLAM with Single Cluster PHD Filters Authors: Lee, Chee Sing; Clark, Daniel; Salvi, Joaquim
    Recent work by Mullane, Vo, and Adams has re-examined the probabilistic foundations of feature-based Simultaneous Localization and Mapping (SLAM), casting the problem in terms of filtering with random finite sets. Algorithms were developed based on Probability Hypothesis Density (PHD) filtering techniques that provided superior performance to leading feature-based SLAM algorithms in challenging mea- surement scenarios with high false alarm rates, high missed detection rates, and high levels of measurement noise. We investigate this approach further by considering a hierarchical point process, or single-cluster multi-object, model, where we consider the state to consist of a map of landmarks conditioned on a vehicle state. Using Finite Set Statistics, we are able to find tractable formulae to approximate the joint vehicle-landmark state based on a single Poisson multi-object assumption on the predicted density. We describe the single-cluster PHD filter and the practical implementation developed based on a particle-system representation of the vehicle state and a Gaussian mixture approximation of the map for each particle. Synthetic simulation results are presented to compare the novel algorithm against the previous PHD filter SLAM algorithm. Results presented indicate a superior performance in vehicle and map landmark localization, and comparable performance in landmark cardinality estimation.
  • Simultaneous Localization and Scene Reconstruction with Monocular Camera Authors: Huang, Kuo- Chen; Tseng, Shih-Huan; Mou, Wei-Hao; Fu, Li-Chen
    In this paper, we propose an online scene recon- struction algorithm with monocular camera since there are many advantages on modeling and visualization of an environ- ment with physical scene reconstruction instead of resorting to sparse 3D points. The goal of this algorithm is to simultaneously track the camera position and map the 3D environment, which is close to the spirit of visual SLAM. There’re plenty of visual SLAM algorithms in the current literature which can provide a high accuracy performance, but many of them rely on stereo cameras. It’s true that we’ll face many more challenges to accomplish this task with monocular camera. However, the advantages of cheaper and easier deployable hardware setting have made monocular approach more attractive. Specifically, we apply a maximum a posteriori Bayesian approach with optimization technique to simultaneously track the camera and build a dense point cloud. We also propose a feature expansion method to expand the density of points, and then online reconstruct the scene with a delayed approach. Furthermore, we utilize the reconstructed model to accomplish visual localization task without extracting the features. Finally, a number of experiments have been conducted to validate our proposed approach, and promising performance can be observed.
  • Rhythm-based Adaptive Localization in Incomplete RFID Landmark Environments Authors: Kodaka, Kenri; Ogata, Tetsuya; Sugano, Shigeki
    This paper proposes a novel hybrid-structured model for the adaptive localization of robots combining a stochastic localization model and a rhythmic action model, for avoiding vacant spaces of landmarks efficiently. In regularly arranged landmark environments, robots may not be able to detect any landmarks for a long time during a straight-like movement. Consequently, locally diverse and smooth movement patterns need to be generated to keep the position estimation stable. Conventional approaches aiming at the probabilistic optimization cannot rapidly generate the detailed movement pattern due to a huge computational cost; therefore a simple but diverse movement structure needs to be introduced as an alternative option. We solve this problem by combining a particle filter as the stochastic localization module and the dynamical action model generating a zig-zagging motion. The validation experiments, where virtual-line-tracing tasks are exhibited on a floor-installed RFID environment, show that introducing the proposed rhythm pattern can improve a minimum error boundary and a velocity performance for arbitrary tolerance errors can be improved by the rhythm amplitude adaptation fed back by the localization deviation.

Mobile Manipulation: Planning & Control

  • Planning with Adaptive Dimensionality for Mobile Manipulation Authors: Gochev, Kalin; Safonova, Alla; Likhachev, Maxim
    Mobile manipulation planning is a hard problem composed of multiple challenging sub-problems, some of which require searching through large high-dimensional state-spaces. The focus of this work is on computing a trajectory to safely maneuver an object through an environment, given the start and goal configurations. In this work we present a heuristic search-based deterministic mobile manipulation planner, based on our recently-developed algorithm for planning with adaptive dimensionality. Our planner demonstrates reasonable performance, while also providing strong guarantees on completeness and suboptimality bounds with respect to the graph representing the problem.
  • Unifying Perception, Estimation and Action for Mobile Manipulation Via Belief Space Planning Authors: Kaelbling, Leslie; Lozano-Perez, Tomas
    In this paper, we describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains. The strategy is based on planning in the belief space of probability distribution over states. Our planning approach is based on hierarchical symbolic regression (pre-image back-chaining). We develop a vocabulary of fluents that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators lead to task-oriented perception in support of the manipulation goals.
  • Distributed Cooperative Object Attitude Manipulation Authors: Markdahl, Johan; Karayiannidis, Yiannis; Hu, Xiaoming; Kragic, Danica
    This paper proposes a local information based control law in order to solve the planar manipulation problem of rotating a grasped rigid object to a desired orientation using multiple mobile manipulators. We adopt a multi-agent systems theory approach and assume that: (i) the manipulators (agents) are capable of sensing the relative position to their neighbors at discrete time instances, (ii) neighboring agents may exchange information at discrete time instances, and (iii) the communication topology is connected. Control of the manipulators is carried out at a kinematic level in continuous time and utilizes inverse kinematics. The mobile platforms are assigned trajectory tracking tasks that adjust the positions of the manipulator bases in order to avoid singular arm configurations. Our main result concerns the stability of the proposed control law.
  • A Hybrid Control for Automatic Docking of Electric Vehicles for Recharging Authors: Petrov, Plamen; Boussard, clément; Ammoun, Samer; Nashashibi, Fawzi
    In this paper, we present the architecture of an innovative docking station for electric vehicles recharging and a hybrid control scheme for automatic docking of the vehicles. This work is a part of on-going project concerning the development of a smart charging station for electric vehicles equipped with an automated arm, which connect the vehicle to the charging station, and an infrared beacon system for localizing the automatically maneuvering vehicle in the docking area. The proposed control scheme combines time-optimal (bang-bang) control with continuous time-invariant nonlinear control, which stabilizes the vehicle to a small neighborhood of the docking point. Simulation and experimental results illustrate the effectiveness of the proposed controller
  • On Continuous Null Space Projections for Torque-Based, Hierarchical, Multi-Objective Manipulation Authors: Dietrich, Alexander; Albu-Schäffer, Alin; Hirzinger, Gerd
    The technological progress in the field of robotics results in more and more complex manipulators. However, having an increasing number of degrees of freedom raises the question of how to use them effectively. In turn, establishing manipulators in human environments, e.g., as service robots, calls for the fulfillment of various constraints and tasks at the same time. In the context of torque controlled robotic systems, we provide an approach to simultaneously deal with a multitude of tasks and constraints which are arranged in a hierarchy, utilizing the large number of actuated joints of the manipulator. To this end, we propose a continuous null space projection technique to consider unilateral constraints, singular Jacobian matrices and dynamic variations of the priority order within the hierarchical structure. We show that activating and deactivating tasks as well as crossing singularities does not lead to a discontinuous control law. Simulations and experiments on the humanoid Justin of the German Aerospace Center (DLR) validate our approach. The presented concept is supposed to contribute to whole-body control frameworks.