Paloma de la Puente

Feature based graph-SLAM in structured environments

Introducing a priori knowledge about the latent structure of the environment in simultaneous localization and mapping (SLAM), can improve the quality and consistency results of its solutions. In this paper we describe and analyze a general framework for the detection, evaluation, incorporation and removal of structure constraints into a feature-based graph formulation of SLAM. We specifically show how including different kinds and levels of features in a hierarchical manner allows the system to easily discover new structure and why it makes more sense than other possible representations. The main algorithm in this framework follows an expectation maximization approach to iteratively infer the most probable structure and the most probable map. Experimental results show how this approach is suitable for the integration of a large variety of constraints and how our method can produce nice and consistent maps in regular environments.

A Vision-based Quadrotor Multi-robot Solution for the Indoor Autonomy Challenge of the 2013 International Micro Air Vehicle Competition

This paper presents a completely autonomous solution to participate in the Indoor Challenge of the 2013 International Micro Air Vehicle Competition (IMAV 2013). Our proposal is a multi-robot system with no centralized coordination whose robotic agents share their position estimates. The capability of each agent to navigate avoiding collisions is a consequence of the resulting emergent behavior. Each agent consists of a ground station running an instance of the proposed architecture that communicates over WiFi with an AR Drone 2.0 quadrotor. Visual markers are employed to sense and map obstacles and to improve the pose estimation based on Inertial Measurement Unit (IMU) and ground optical flow data. Based on our architecture, each robotic agent can navigate avoiding obstacles and other members of the multi-robot system. The solution is demonstrated and the achieved navigation performance is evaluated by means of experimental flights. This work also analyzes the capabilities of the presented solution in simulated flights of the IMAV 2013 Indoor Challenge. The performance of the CVG_UPM team was awarded with the First Prize in the Indoor Autonomy Challenge of the IMAV 2013 competition.

Extraction of Geometrical Features in 3D Environments for Service Robotic Applications

Modeling environments with 3D feature based representations is a challenging issue in current mobile robotics. Fast and robust algorithms are required for applicability to navigation. We present an original and effective segmentation method that uses computer vision techniques and the residuals from plane fitting as measurements to generate a range image from 3D data acquired by a laser scanner. The extracted points of each region are converted into plane patches, spheres and cylinders by means of least-squares fitting.

Impact of two adjustable-autonomy models on the scalability of single-human/multiple-robot teams for exploration missions.

Objective: The aim of this study was to evaluate two models for adjusting autonomy in mobile robots to find out the best way for the operator to interact with the system with as many robots as possible. The first model is the most used in mobile robots; the second proposes a flexible autonomy management. Background: There are different ways of adjusting the autonomy level in man-machine systems: adjustable autonomy, in which the operator has the initiative over the autonomy level; adaptive autonomy, in which the autonomy level is adjusted depending on the task and context; and mixed initiatives. One of the drawbacks of using adjustable autonomy is that it is claimed not to be flexible enough, resulting in a high operator workload. We propose and evaluate a flexible adjustable autonomy model for robot-team supervision. Method: Two experiments were designed to test the scalability and performance of the man-machine system with two alternative configurations for the autonomy management. The independent variable is the number of robots, and the measured variable is the man-machine system performance. The experiments are between subjects. We have used ANOVA and Bonferroni post hoc analysis for analyzing the results. Results: On the basis of these analyses, we conclude that a flexible adjustable autonomy model results in better performance than the classic, rigid one, in which the operator directly chooses the autonomy level. Conclusion: Flexible autonomy adjustment permits one operator to control a team of robots with better results in terms of performance and robot use, as he or she can directly act at the error level, leaving the responsibility of readjusting and resuming the task to the system and hence reducing the operator’s workload. Application: The results can be applied to exploration robotics, mainly, in which one operator controls a team of robots. In general, these principles can be extended to other single-man/multiple-machine systems.

Visual Quadrotor Swarm for the IMAV 2013 Indoor Competition

This paper presents a low-cost framework for visual quadrotor swarm prototyping which will be utilized to participate in the 2013 International Micro Air Vehicle Indoor Flight Competition. The testbed facilitates the swarm design problem by utilizing a cost-efficient quadrotor platform, the Parrot AR Drone 2.0; by using markers to simplify the visual localization problem, and by broadcoasting the estimated location of the swarm members to obviate the partner dectection problem. The development team can then focus their attention on the design of a succesful swarming behaviour for the problem at hand. ArUco Codes [2] are used to sense and map obstacles and to improve the pose estimation based on the IMU data and optical flow by means of an Extended Kalman Filter localization and mapping method. A free-collision trajectory for each drone is generated by using a combination of well-known trajectory planning algorithms: probabilistic road maps, the potential field map algorithm and the A-Star algorithm. The control loop of each drone of the swarm is closed by a robust mid-level controller. A very modular design for integration within the Robot Operating System (ROS) [13] is proposed.

Evaluating a human-robot interface for exploration missions

The research reported in this paper concerns the design, implementation, and experimental evaluation of a Human-Robot Interface for stationary remote operators, implemented for a PC computer. The GUI design and functionality is described. An Autonomy Management Model has been implemented and explained. We have conducted user evaluation, making two set of experiments, that will be described and the resulting data analyzed. The conclusions give an insight on the most important usability concerns, regarding the operator situational awareness. The scalability of the interface is also experimentally studied.

Feature based graph SLAM with high level representation using rectangles

In mobile robotics, feature based maps are very popular for the representation of the environment. Some of the main advantages of these maps are final compactness and expressivity, aspects that make storage easier and simplify higher level reasoning. Most existing approaches, however, stick to low level features such as points, segments and sometimes circles, corners or splines. This paper presents the incorporation of rectangles as higher level features in a feature based graph Simultaneous Localization and Mapping (SLAM) framework for the consideration of the structure of the environment in the mapping process. A new method for building maps of rectangles in mobile robotics is proposed.A generic feature based graph SLAM framework for different types of features and extraction methods is used.Incorrect structure constraints can be removed.Rectangles can be detected from segments obtained from laser data even in conditions of only partial visibility.Properties such as parallelism and orthogonality can be preserved, and the resulting maps are compact and convenient for human interpretation.

Dense map inference with user-defined priors: from priorlets to scan eigenvariations

When mapping is formulated in a Bayesian framework, the need of specifying a prior for the environment arises naturally. However, so far, the use of a particular structure prior has been coupled to working with a particular representation. We describe a system that supports inference with multiple priors while keeping the same dense representation. The priors are rigorously described by the user in a domain-specific language. Even though we work very close to the measurement space, we are able to represent structure constraints with the same expressivity as methods based on geometric primitives. This approach allows the intrinsic degrees of freedom of the environments shape to be recovered. Experiments with simulated and real data sets will be presented.

Exploratory Analysis of Operator: Robot Ratio in Search and Rescue Missions

Abstract Mobile robots are increasingly becoming an aid to humans in accomplishing dangerous tasks. Examples of such tasks include search and rescue missions, military missions, surveillance, scheduled operations (such as checking the reactor of a nuclear plant), and so forth. The advantage of using robots in such situations is that they accomplish high-risk task without exposing humans to danger: robots go where humans fear to tread. When dealing with robot teams for exploration missions, the human operator needs to control simultaneously multiple robots. This paradigm highly increases the difficulty of the operator task. In this paper, based on extensive experimental data, we are studying the problematic associated to the Single- Operator-Multiple-Robot Interaction. This analysis can help to ascertain the critical points in multi-robot control and supervision. The experiments have been done in a Search and Rescue scenario using USARSim.

Hobbit: Providing Fall Detection and Prevention for the Elderly in the Real World

We present the robot developed within the Hobbit project, a socially assistive service robot aiming at the challenge of enabling prolonged independent living of elderly people in their own homes. We present the second prototype (Hobbit PT2) in terms of hardware and functionality improvements following first user studies. Our main contribution lies within the description of all components developed within the Hobbit project, leading to autonomous operation of 371 days during field trials in Austria, Greece, and Sweden. In these field trials, we studied how 18 elderly users (aged 75 years and older) lived with the autonomously interacting service robot over multiple weeks. To the best of our knowledge, this is the first time a multifunctional, low-cost service robot equipped with a manipulator was studied and evaluated for several weeks under real-world conditions. We show that Hobbit’s adaptive approach towards the user increasingly eased the interaction between the users and Hobbit. We provide lessons learned regarding the need for adaptive behavior coordination, support during emergency situations, and clear communication of robotic actions and their consequences for fellow researchers who are developing an autonomous, low-cost service robot designed to interact with their users in domestic contexts. Our trials show the necessity to move out into actual user homes, as only there can we encounter issues such as misinterpretation of actions during unscripted human-robot interaction.