Monte Carlo Localization in Hand-Drawn Maps

Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80

An integrated probabilistic model for scan-matching, moving object detection and motion estimation

Abstract — This paper presents a novel framework for in-tegrating fundamental tasks in robotic navigation through a statistical inference procedure. A probabilistic model that jointly reasons about scan-matching, moving object detection and their motion estimation is developed. Scan-matching and moving object detection are two important problems for full autonomy of robotic systems in complex dynamic environments. Popular techniques for solving these problems usually address each task in turn disregarding important dependencies. The model developed here jointly reasons about these tasks by performing inference in a probabilistic graphical model. It allows different but related problems to be expressed in a single framework. The experiments demonstrate that jointly reasoning results in better estimates for both tasks compared to solving the tasks individually. I

Heterogeneous Feature State Estimation with Rao-Blackwellized Particle Filters

In this paper we present a novel technique to estimate the state of heterogeneous features from inaccurate sensors. The proposed approach exploits the reliability of the feature extraction process in the sensor model and uses a RaoBlackwellized particle filter to address the data association problem. Experimental results show that the use of reliability improves performance by allowing the approach to perform better data association among detected features. Moreover, the method has been tested on a real robot during an exploration task in a non-planar environment. This last experiment shows an improvement in correctly detecting and classifying interesting features for navigation purpose. © 2007 IEEE

An Experimental Protocol for Benchmarking Robotic Indoor Navigation

Abstract. Robot navigation is one of the most studied problems in robotics and the key capability for robot autonomy. Navigation tech-niques have become more and more reliable, but evaluation mainly fo-cused on individual navigation components (i.e., mapping, localization, and planning) using datasets or simulations. The goal of this paper is to define an experimental protocol to evaluate the whole navigation system, deployed in a real environment. To ensure repeatability and reproducibil-ity of experiments, our benchmark protocol provides detailed definitions and controls the environment dynamics. We define standardized environ-ments and introduce the concept of a reference robot to allow comparison between different navigation systems at different experimentation sites. We present applications of our protocol in experiments in two different research groups, showing the usefulness of the benchmark

Motion clustering and estimation with conditional random fields

Abstract—Moving objects are present in many robotic appli-cations. An accurate detection and motion estimation of these objects can be crucial for the success and safety of the robot and people surrounding it. This paper presents a new probabilistic framework for clustering dependent or relational data, applied to the problem of motion clustering and estimation. While conventional techniques such as scan differencing perform well in many cases, they usually assume that a good pose estimate is available and fail when points belonging to dynamic objects show some overlap in consecutive readings. The technique proposed, CRF-Clustering, by explicitly reasoning about the underlying motion of the object, is able to deal with poor initial motion estimate and overlapping points. Moreover, it is able to consider the dependencies between neighbor points in the scans to reduce the noise in the clustering assignment. The model parameters can be estimated from labeled data in a statistically sound learning procedure. Experiments show that CRF-Clustering is able to detect moving objects, cluster them and estimate their motion. I

Planning Problems for Social Robots

As robots enter environments that they share with people, human-aware planning and interaction become key tasks to be addressed. For doing so, robots need to reason about the places and times when and where humans are engaged into which activity and plan their actions accordingly. In this paper, we first address this issue by learning a nonhomogenous spatial Poisson process whose rate function encodes the occurrence probability of human activities in space and time. We then present two planning problems for human robot interaction in social environments. The first one is the maximum encounter probability planning problem, where a robot aims to find the path along which the probability of encountering a person is maximized. We are interested in two versions of this problem, with deadlines or with a certainty quota. The second one is the minimum interference coverage problem, where a robot performs a coverage task in a socially compatible way by reducing the hindrance or annoyance caused to people. An example is a noisy vacuum robot that has to cover the whole apartment having learned that at lunch time the kitchen is a bad place to clean. Formally, the problems are time dependent variants of known planning problems: MDPs and price collecting TSP for the first problem and the asymmetric TSP for the second. The challenge is that the cost functions of the arcs and nodes vary with time, and that execution time is more important that optimality, given the real-time constraints in robotic systems. We present experimental results using variants of known planners and formulate the problems as benchmarks to the community

Learning to Guide Random Tree Planners in High Dimensional Spaces

Fig. 1 . Example paths for a mobile manipulation platform computed with RRT-Connect Abstract-In this paper we present the projection and bias heuristic (PBH), a motion planning algorithm that makes use of low-dimensional projections to improve sampling-based planning algorithms. In contrast to other state-of-the-art methods, we do not assume that projections are either random or given by an expert user. Rather, our goal is to learn projections such that planning on them improves the efficiency and the quality of solutions. We present both, a method to learn effective projections and a sampling algorithm that makes use of them. We show that our approach can be easily integrated into popular sampling-based planners. Extensive experiments performed in simulated environments demonstrate that our approach produces paths that are in general shorter than those obtained with state-of-the-art algorithms. Moreover, it generally requires less computation time