TechTalks from event: Technical session talks from ICRA 2012

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SLAM II

  • Efficient Data-Driven MCMC Sampling for Vision-Based 6D SLAM Authors: Min, Jihong; Kim, Jungho; Shin, Seunghak; Kweon, In So
    In this paper, we propose a Markov Chain Monte Carlo (MCMC) sampling method with the data-driven proposal distribution for six-degree-of-freedom (6-DoF) SLAM. Recently, visual odometry priors have been widely used as the process model in the SLAM formulation to improve the SLAM performance. However, modeling the uncertainties of incremental motions estimated by visual odometry is especially difficult under challenging conditions, such as erratic motion. For a particle-based model representation, it can represent the uncertainty of the camera motion well under erratic motion compared to the constant velocity model or a Gaussian noise model, but the manner of representing the proposal distribution and sampling the particles is extremely important, as we can maintain only a limited number of particles in the high-dimensional state space. Hence, we propose an effective sampling approach by exploiting MCMC sampling and the data-driven proposal distribution to propagate the particles. We demonstrate the performance of the proposed approach for 6-DoF SLAM using both synthetic and real datasets and compare the performance with those of other sampling methods.
  • Scan Segments Matching for Pairwise 3D Alignment Authors: Douillard, Bertrand; Quadros, Alastair James; Morton, Peter; Underwood, James Patrick; De Deuge, Mark; Hugosson, Simon; Hallström, Manfred; Bailey, Tim
    This paper presents a method for pairwise 3D alignment which solves data association by matching scan segments across scans. Generating accurate segment associations allows to run a modified version of the Iterative Closest Point (ICP) algorithm where the search for point-to-point correspondences is constrained to associated segments. The novelty of the proposed approach is in the segment matching process which takes into account the proximity of segments, their shape, and the consistency of their relative locations in each scan. Scan segmentation is here assumed to be given (recent studies provide various alternatives). The method is tested on seven sequences of Velodyne scans acquired in urban environments. Unlike various other standard versions of ICP, which fail to recover correct alignment when the displacement between scans increases, the proposed method is shown to be robust to displacements of several meters. In addition, it is shown to lead to savings in computational times which are potentially critical in real-time applications.
  • Planar Surface SLAM with 3D and 2D Sensors Authors: Trevor, Alexander J B; Rogers III, John G.; Christensen, Henrik Iskov
    We present an extension to our feature based mapping technique that allows for the use of planar surfaces such as walls, tables, counters, or other planar surfaces as landmarks in our mapper. These planar surfaces are measured both in 3D point clouds, as well as 2D laser scans. These sensing modalities compliment each other well, as they differ significantly in their measurable fields of view and maximum ranges. We present experiments to evaluate the contributions of each type of sensor.
  • Uncertainty Estimation for a 6-DoF Spectral Registration Method As Basis for Sonar-Based Underwater 3D SLAM Authors: Pfingsthorn, Max; Birk, Andreas; Buelow, Heiko
    An uncertainty estimation method for 6 degree of freedom (6-DoF) spectral registration is introduced here. The underlying 6-DoF registration method based on Phase Only Matched Filtering (POMF) is capable of dealing with very noisy sensor data. It is hence well suited for 3D underwater mapping, where relatively inaccurate sonar imaging devices have to be employed. An uncertainty estimation method is required to use this registration method in a Simultaneous Localization and Mapping (SLAM) framework. To our knowledge, the first such method for 6-DoF spectral registration is presented here. This new uncertainty estimation method treats the POMF results as probability mass functions (PMF). Due to the decoupling in the underlying method, yaw is computed by a one-dimensional POMF leading hence to a 1D PMF; roll and pitch are simultaneously computed and hence encoded in a 2D PMF. Furthermore, a 3D PMF is generated for the translation as it is determined by a 3D POMF. A normal distribution is fitted on each of the PMF to get the uncertainty estimate. The method is experimentally evaluated with simulated as well as real world sonar data. It is shown that it indeed can be used for SLAM, which significantly improves the map quality.
  • Interactive Acquisition of Residential Floor Plans Authors: Kim, Young Min; Dolson, Jennifer; Sokolsky, Michael; Koltun, Vladlen; Thrun, Sebastian
    We present a hand-held system for real-time, interactive acquisition of residential floor plans. The system integrates a commodity range camera, a micro-projector, and a button interface for user input and allows the user to freely move through a building to capture its important architectural elements. The system uses the Manhattan world assumption, which posits that wall layouts are rectilinear. This assumption allows generating floor plans in real time, enabling the operator to interactively guide the reconstruction process and to resolve structural ambiguities and errors during the acquisition. The interactive component aids users with no architectural training in acquiring wall layouts for their residences. We show a number of residential floor plans reconstructed with the system.
  • CFastSLAM: A New Jacobian Free Solution to SLAM Problem Authors: Song, Yu; Li, Qingling; Kang, Yifei
    While FastSLAM algorithm is a popular solution to SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution; the other is errors accumulation caused by inaccuracy linearization of the robot motion model and the observation model. To overcome the problems, we propose a new Jacobian free CFastSLAM algorithm. The main contribution of this work lies in the utilization of Cubature Kalman Filter (CKF), which calculate Gaussian Weight Integral based on Cubature Rule, to design an optimal proposal distribution of the particle filter and to estimate the environment feature landmarks. On the basis of Rao-Blackwellized particle filter, proposed algorithm is comprised by two main parts: in the first part, a Cubature Particle Filter (CPF) is derived to localize the robot; in the second part, a set of CKFs is used to estimate the environment landmarks. The performance of the CFastSLAM is investigated and compared with that of FastSLAM2.0 and UFastSLAM in simulations and experiments. Results verify that the CFastSLAM improves SLAM performance.

Intelligent Manipulation Grasping

  • A Generalized Framework for Opening Doors and Drawers in Kitchen Environments Authors: Ruehr, Thomas; Sturm, Jürgen; Pangercic, Dejan; Beetz, Michael; Cremers, Daniel
    In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i.e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51.9% of all 104 trials. With this work, we contribute a well-tested building block of open-source software for future robotic service applications.
  • FCL: A General Purpose Library for Collision and Proximity Queries Authors: Pan, Jia; Chitta, Sachin; Manocha, Dinesh
    We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.
  • Learning Organizational Principles in Human Environments Authors: Schuster, Martin Johannes; Jain, Dominik; Tenorth, Moritz; Beetz, Michael
    In the context of robotic assistants in human everyday environments, pick and place tasks are beginning to be competently solved at the technical level. The question of where to place objects or where to pick them up from, among other higher-level reasoning tasks, is therefore gaining practical relevance. In this work, we consider the problem of identifying the organizational structure within an environment, i.e. the problem of determining organizational principles that would allow a robot to infer where to best place a particular, previously unseen object or where to reasonably search for a particular type of object given past observations about the allocation of objects to locations in the environment. This problem can be reasonably formulated as a classification task. We claim that organizational principles are governed by the notion of similarity and provide an empirical analysis of the importance of various features in datasets describing the organizational structure of kitchens. For the aforementioned classification tasks, we compare standard classification methods, reaching average accuracies of at least 79% in all scenarios. We thereby show that ontology-based similarity measures are well-suited as highly discriminative features. We demonstrate the use of learned models of organizational principles in a kitchen environment on a real robot system, where the robot identifies a newly acquired item, determines a suitable location and then stores the item accordingly.
  • Using Manipulation Primitives for Brick Sorting in Clutter Authors: Gupta, Megha; Sukhatme, Gaurav
    This paper explores the idea of manipulation-aided perception and grasping in the context of sorting small objects on a tabletop. We present a robust pipeline that combines perception and manipulation to accurately sort Duplo bricks by color and size. The pipeline uses two simple motion primitives to manipulate the scene in ways that help the robot to improve its perception. This results in the ability to sort cluttered piles of Duplo bricks accurately. We present experimental results on the PR2 robot comparing brick sorting without the aid of manipulation to sorting with manipulation primitives that show the benefits of the latter, particularly as the degree of clutter in the environment increases.
  • A constraint-based programming approach to physical human-robot Interaction Authors: Borghesan, Gianni; Willaert, Bert; De Schutter, Joris
    Abstract— This work aims to extend the constraint-based formalism iTaSC for scenarios where physical human-robot interaction plays a central role, which is the case for e.g. surgical robotics, rehabilitation robotics and household robotics. To really exploit the potential of robots in these scenarios, it should be possible to enforce force and geometrical constraints in an easy and flexible way. iTaSC allows to express such constraints in different frames expressed in arbitrary spaces and to obtain control setpoints in a systematic way. In previous implementations of iTaSC, industrial velocity-controlled robots were considered. This work presents an extension of the iTaSC-framework that allows to take advantage of the back-drivability of a robot thus avoiding the use of force sensors. Then, as a case-study, the iTaSC-framework is used to formulate a (position-position) teleoperation scheme. The theoretical findings are experimentally validated using a PR2 robot.

Formal Methods

  • Temporal Logic Motion Control Using Actor-Critic Methods Authors: Ding, Xu Chu; Wang, Jing; Lahijanian, Morteza; Paschalidis, Yannis; Belta, Calin
    In this paper, we consider the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov Decision Process (MDP). The robot control problem becomes finding the control policy maximizing the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each state-action pair, as well as solving the necessary optimization problem for the optimal policy are usually not computationally feasible. To address these issues, we propose an approximate dynamic programming framework based on a least-square temporal difference learning method of the actor-critic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Hardware-in-the-loop simulations confirm that convergence of the parameters translates to an approximately optimal policy.
  • Robust Multi-Robot Optimal Path Planning with Temporal Logic Constraints Authors: Ulusoy, Alphan; Smith, Stephen L.; Ding, Xu Chu; Belta, Calin
    In this paper we present a method for automatically planning robust optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the environment is modeled as a weighted transition system, and the mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied by the regions of the environment. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition while ensuring that the LTL formula is satisfied even with uncertainty in the robots' traveling times. We characterize a class of LTL formulas that are robust to robot timing errors, for which we generate optimal paths if no timing errors are present, and we present bounds on the deviation from the optimal values in the presence of errors. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.
  • Stunt Driving via Policy Search Authors: Lau, Tak Kit; Liu, Yunhui
    To explore or exploit? In this paper, we discuss the long-standing exploration-exploration dilemma in context of designing a learning controller for stunt-style driving with scarce samples. By making an efficient use of a single demonstration by an expert, our algorithm leverages our intuitive understanding of driving to extract a coarse dynamics model from the collected driving data, then formulate the policy search in a setting of gradient update with a specially designed cost function. Both theoretical and empirical results are detailed and discussed.
  • Probabilistic Control from Time-Bounded Temporal Logic Specifications in Dynamic Environments Authors: Medina Ayala, Ana Ivonne; Andersson, Sean; Belta, Calin
    The increasing need for real time robotic systems capable of performing tasks in changing and constrained environments demands the development of reliable and adaptable motion planning and control algorithms. This paper considers a mobile robot whose performance is measured by the completion of temporal logic tasks within a certain period of time. In addition to such time constraints, the planning algorithm must also deal with changes in the robot’s workspace during task execution. In our case, the robot is deployed in a partitioned environment subjected to structural changes in which doors shift from open to closed and vice-versa. The motion of the robot is modeled as a Continuous Time Markov Decision Process and the robot’s mission is expressed as a Continuous Stochastic Logic (CSL) temporal logic specification. An approximate solution to find a control strategy that satisfies such specifications is derived for a subset of probabilistic CSL formulae. Simulation and experimental results are provided to illustrate the method.
  • Non-Gaussian Belief Space Planning: Correctness and Complexity Authors: Platt, Robert; Tedrake, Russ; Kaelbling, Leslie; Lozano-Perez, Tomas
    We consider the partially observable control problem where it is potentially necessary to perform complex information-gathering operations in order to localize state. One approach to solving these problems is to create plans in {em belief-space}, the space of probability distributions over the underlying state of the system. The belief-space plan encodes a strategy for performing a task while gaining information as necessary. Unlike most approaches in the literature which rely upon representing belief state as a Gaussian distribution, we have recently proposed an approach to non-Gaussian belief space planning based on solving a non-linear optimization problem defined in terms of a set of state samples~cite{platt_isrr2011}. In this paper, we show that even though our approach makes optimistic assumptions about the content of future observations for planning purposes, all low-cost plans are guaranteed to gain information in a specific way under certain conditions. We show that eventually, the algorithm is guaranteed to localize the true state of the system and to reach a goal region with high probability. Although the computational complexity of the algorithm is dominated by the number of samples used to define the optimization problem, our convergence guarantee holds with as few as two samples. Moreover, we show empirically that it is unnecessary to use large numbers of samples in order to obtain good performance.
  • Proving the Correctness of Concurrent Robot Software Authors: Kazanzides, Peter; Kouskoulas, Yanni; Deguet, Anton; Shao, Zhong
    Component-based software has been proposed as a methodology for improving software reuse and has increasingly been adopted by robot software developers. At the same time, robot systems typically have real-time performance requirements and performance gains can often be obtained by multi-threading. It is challenging, however, to create correct multi-threaded software, especially when standard mutual exclusion primitives, such as mutexes and semaphores, are eschewed in favor of more efficient, lock-free mechanisms. It is even more difficult to find these errors, as they can remain dormant for years until triggered by just the &quot;right&quot; conditions. Our approach, therefore, is to apply Formal Methods to reason about the correctness of these mechanisms. As a first step, we adopted a recently-developed program logic called History for Local Rely/Guarantee (HLRG) and applied it to prove the correctness (after first finding and fixing an error) of one such mechanism in the open source <i>cisst</i> software package. This strategy is not specific to <i>cisst</i> and can be applied to other packages.