List of all recorded talks

  • Indexing Visual Features: Real-Time Loop Closure Detection Using a Tree Structure Authors: Liu, Yang; Zhang, Hong
    We propose a simple and effective method for visual loop closure detection in appearance-based robot SLAM. Unlike the Bag-of-Words (BoW hereafter) approach in most existing work of the problem, our method uses direct feature matching to detect loop closures and therefore avoid the perceptual aliasing problem caused by the vector quantization process of BoW. We show that a tree structure can be efficient in online loop closure detection. In our method, a KD-tree is built over all the key frame features and an indexing table is kept for retrieving relevant key frames. Due to the efficiency of the tree-based feature matching, loop closure detection can be achieved in real-time. To investigate the scalability of the method, we also apply the scale dependent feature selection in our method and show that the run time can be reduced significantly at the expense of sacrificing the performance to some extent. The proposed method is validated on an indoor SLAM dataset with 7,420 images.
  • The Speed Assignment Problem for Conflict Resolution in Aerial Robotics Authors: Alejo, David; Cobano, Jose A.; Trujillo, Miguel Angel; Viguria, Antidio; Ollero, Anibal
    This paper presents an efficient conflict resolution method for multiple aerial vehicles based on speed planning. The problem is assigning a speed profile to each aerial vehicle in real time such that the separation between them is greater than a minimum safety value and the total deviation from the initial planned trajectories is minimized. Also, the arrival time of each aerial vehicle at the end waypoint of the trajectory is taken into account to solve the conflicts. The proposed method involves the use of appropriate airspace discretization. The method consists of two steps: a search tree step, which finds if it exists a solution; and an optimization step by solving a QP-problem, which minimizes a cost function. The paper also presents simulations for several scenarios and experiments that have been carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).
  • Incremental Probabilistic Geometry Estimation for Robot Scene Understanding Authors: Cahier, Louis-Kenzo; Ogata, Tetsuya; Okuno, Hiroshi G.
    Our goal is to give mobile robots a rich representation of their environment as fast as possible. Current mapping methods such as SLAM are often sparse, and scene reconstruction methods using tilting laser scanners are relatively slow. In this paper, we outline a new method for iterative construction of a geometric mesh using streaming time-of-flight range data. Our results show that our algorithm can produce a stable representation after 6 frames, with higher accuracy than raw time-of-flight data.
  • Logical Winnowing Methods from Multiple Identification Candidates Using Corresponding Appearance Identification Results in Time-Series Authors: Tanaka, Kazushi; Takeuchi, Eijiro; Ohno, Kazunori; Tadokoro, Satoshi; Yonezawa, Toru
    This paper describes logical winnowing methods from multiple identification candidates using corresponding appearance identification results with chronological pedestrian tracking results. It is difficult to identify individual using appearance identification, because appearance identification has some properties. This research proposes two methods that logically winnow out the identification candidates as methods that effectively fuse different directional results without the directional information. Experiments were made to verify the validity of the proposed methods. A mobile robot equipped with a laser scanner and a camera was used in the experiments. A pedestrian tracking method uses the laser scanner. The appearance identification uses the camera. The experimental results verified the validity of the logical winnowing method taking the logical product of candidates determined by each round of identification. In this paper, the appearance identification properties, the proposed methods and the experiments are described.
  • Probabilistic Depth Image Registration incorporating Nonvisual Information Authors: Wüthrich, Manuel; Pastor, Peter; Righetti, Ludovic; Billard, Aude; Schaal, Stefan
    In this paper, we derive a novel registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which will play a central role in our algorithm. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL cite{rusu11} implementations of feature mapping and ICP, especially if nonvisual information is available.
  • Revisited Dos Samara Unmanned Aerial Vehicle: Design and Control Authors: Alexis, Kostas; Tzes, Anthony
    In this article, the design, system modeling and control of a new hybrid type of Unmanned Aerial Vehicle (UAV) is presented. Based on the flight principles of the Dos Samara UAV, a new vehicle that combines the capability of hovering, like a helicopter, and high speed–increased endurance flying, like a fixed–wing aircraft, is designed. The nonlinear dynamics model of the aircraft operating in helicopter mode is derived and linearized around hovering operation. Based on this model an LQ–controller is designed. The performance of the overall system is examined in simulation studies.
  • Improving the Efficiency of Bayesian Inverse Reinforcement Learning Authors: Michini, Bernard; How, Jonathan
    Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given knowledge of the transition function and a set of expert demonstrations. While many IRL algorithms exist, Bayesian IRL [1] provides a general and principled method of reward learning by casting the problem in the Bayesian inference framework. However, the algorithm as originally presented suffers from several inefficiencies that prohibit its use for even moderate problem sizes. This paper proposes modifications to the original Bayesian IRL algorithm to improve its efficiency and tractability in situations where the state space is large and the expert demonstrations span only a small portion of it. The key insight is that the inference task should be focused on states that are similar to those encountered by the expert, as opposed to making the naive assumption that the expert demonstrations contain enough information to accurately infer the reward function over the entire state space. A modified algorithm is presented and experimental results show substantially faster convergence while maintaining the solution quality of the original method.
  • Learning Diffeomorphisms Models of Robotic Sensorimotor Cascades Authors: Censi, Andrea; Murray, Richard
    The problem of bootstrapping consists in designing agents that can learn from scratch the model of their sensorimotor cascade (the series of robot actuators, the external world, and the robot sensors) and use it to achieve useful tasks. In principle, we would want to design agents that can work for any robot dynamics and any robot sensor(s). One of the difficulties of this problem is the fact that the observations are very high dimensional, the dynamics is nonlinear, and there is a wide range of “representation nuisances” to which we would want the agent to be robust. In this paper, we model the dynamics of sensorimotor cascades using diffeomorphisms of the sensel space. We show that this model captures the dynamics of camera and range-finder data, that it can be used for long-term predictions, and that it can capture nonlinear phenomena such as a limited field of view. Moreover, by analyzing the learned diffeomorphisms it is possible to recover the “linear structure” of the dynamics in a manner which is independent of the commands representation.
  • Interactive Generation of Dynamically Feasible Robot Trajectories from Sketches Using Temporal Mimicking Authors: Luo, Jingru; Hauser, Kris
    This paper presents a method for generating dynamically-feasible, natural-looking robot motion from freehand sketches. Using trajectory optimization, it handles sketches that are too fast, jerky, or pass out of reach by enforcing the constraints of the robot’s dynamic limitations while minimizing the relative temporal differences between the robot’s trajectory and the sketch. To make the optimization fast enough for interactive use, a variety of enhancements are employed including decoupling the geometric and temporal optimizations and methods to select good initial trajectories. The technique is also applicable to transferring human motions onto robots with non-human appearance and dynamics, and we use our method to demonstrate a simulated humanoid imitating a golf swing as well as an industrial robot performing the motion of writing a cursive ”hello” word.
  • A Robot Path Planning Framework That Learns from Experience Authors: Berenson, Dmitry; Abbeel, Pieter; Goldberg, Ken
    We propose a framework, called Lightning, for planning paths in high-dimensional spaces that is able to learn from experience, with the aim of reducing computation time. This framework is intended for manipulation tasks that arise in applications ranging from domestic assistance to robot-assisted surgery. Our framework consists of two main modules, which run in parallel: a planning-from-scratch module, and a module that retrieves and repairs paths stored in a path library. After a path is generated for a new query, a library manager decides whether to store the path based on computation time and the generated path's similarity to the retrieved path. To retrieve an appropriate path from the library we use two heuristics that exploit two key aspects of the problem: (i) A correlation between the amount a path violates constraints and the amount of time needed to repair that path, and (ii) the implicit division of constraints into those that vary across environments in which the robot operates and those that do not. We evaluated an implementation of the framework on several tasks for the PR2 mobile manipulator and a minimally-invasive surgery robot in simulation. We found that the retrieve-and-repair module produced paths faster than planning-from-scratch in over 90% of test cases for the PR2 and in 58% of test cases for the minimally-invasive surgery robot.