Visibility Contractors: Application to Mobile Robot Localization

Visibility is studied and used in several fields: computer graphics, telecommunication, robotics... For instance, in Computer-aided design (CAD) synthesis images are created by simulating light propagation in a scene. Visibility notions are then necessary to compute the visible objects from a point of view, and the shadow of those objects. In mobile robotics the visibility is used for path planning (visibility graph) and localization problems. This presentation is about visibility information for mobile robot localization. The objective is twofold. First a visibility notion based on segment intersections is presented. By considering a set-membership approach it is possible to develop contractors associated to this visibility relation. Then two applications of those visibility contractors to mobile robot localization are presented. The first one corresponds to the pose tracking of a team of robots. The idea is to use a Boolean information (the visibility between two robots: two robots are visible or not) in order to avoid the drifting of those robots (in order to maintain the precision of their position estimations). The second application corresponds to the processing of an original constraint for a set-membership global localization algorithm. This global localization algorithm is based on a CSP approach (Constraint Satisfaction Problem). Adding a visibility constraint to this CSP improves the accuracy of the algorithm

Formations of Localization of Robot Networks

In this paper, we consider the problem of cooperatively localizing a formation of networked mobile robots/vehicles in SE(2), and adapting the formation to reduce localization errors. First, we propose necessary and sufficient conditions to establish when a team of robots with heterogeneous sensors can be completely localized. We present experimental measurements of range and bearing with omni-directional cameras to motivate a simple model for noisy sensory information. We propose a measure of quality of team localization, and show how this measure directly depends on a sensing graph. Finally, we show how the formation and the sensing graph can be adapted to improve the measure of performance for team localization and for localization of targets through experiments and simulations

On the Covariance of ICP-based Scan-matching Techniques

This paper considers the problem of estimating the covariance of roto-translations computed by the Iterative Closest Point (ICP) algorithm. The problem is relevant for localization of mobile robots and vehicles equipped with depth-sensing cameras (e.g., Kinect) or Lidar (e.g., Velodyne). The closed-form formulas for covariance proposed in previous literature generally build upon the fact that the solution to ICP is obtained by minimizing a linear least-squares problem. In this paper, we show this approach needs caution because the rematching step of the algorithm is not explicitly accounted for, and applying it to the point-to-point version of ICP leads to completely erroneous covariances. We then provide a formal mathematical proof why the approach is valid in the point-to-plane version of ICP, which validates the intuition and experimental results of practitioners.Comment: Accepted at 2016 American Control Conferenc

Modeling and stochastic optimization of complete coverage under uncertainties in multi-robot base placements

© 2016 IEEE. Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a realworld object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities

Mobile Robot Localization Using Bar Codes as Artificial Landmarks

"Where am I' is the central question in mobile robot navigation. Robust and reliable localization are of vital importance for an autonomous mobile robot because the ability to constantly monitor its position in an unpredictable, unstructured, and dynamic environment is the essential prerequisite to build up and/or maintain environmental maps consistently and to perform path planning. Thus, selflocalization as precondition for goal-oriented behavior is a fundamental property an autonomous mobile robot needs to be equipped with. Accurate, flexible and low-cost localization are important issues for achieving autonomous and cooperative motions of mobile robots. Mobile robots usually perform self-localization by combining position estimates obtained from odometry or inertial navigation with external sensor data. The objective of the thesis is to present a pragmatic idea which utilizes a camera-based bar code recognition technique in order to support mobile robot localization In indoor environments. The idea is to further improve already existing localization capabilities, obtained from dead-reckoning, by furnishing relevant environmental spots such as doors, stairs, etc. with semantic information. In order to facilitate the detection of these landmarks the employment of bar codes is proposed. The important contribution of the thesis is the designing of two software programs. The first program is the bar code generation program which is able to generate five types of bar code labels that play a major role in the proposed localization method. The second program is the bar code recognition program that analyzes image files looking for a bar code label. If a label is found the program recognizes it and display both the information it contains and its coding type. Results concerning the generation of five types of bar code labels which are code 2 of 5, code 3 of9 , codabar code, code 128 and code 2 of 5 interleaved and the detection and identification of these labels from image files are obtained. In conclusion the thesis proposes a solution to mobile robot self-localization problem, which is the central significant for implementing an autonomous mobile robot, by utilizing a camera-based bar code recognition technique to support the basic localization capabilities obtained from a dead-reckoning method in an indoor environment

Deep Network Uncertainty Maps for Indoor Navigation

Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full occupancy of complex objects such as tables. Deep Neural Networks have recently been proposed to overcome this limitation by learning to estimate object occupancy. These estimates are nevertheless subject to uncertainty, making the evaluation of their confidence an important issue for these measures to be useful for autonomous navigation and mapping. In this work we approach the problem from two sides. First we discuss uncertainty estimation in deep models, proposing a solution based on a fully convolutional neural network. The proposed architecture is not restricted by the assumption that the uncertainty follows a Gaussian model, as in the case of many popular solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout. We present results showing that uncertainty over obstacle distances is actually better modeled with a Laplace distribution. Then, we propose a novel approach to build maps based on Deep Neural Network uncertainty models. In particular, we present an algorithm to build a map that includes information over obstacle distance estimates while taking into account the level of uncertainty in each estimate. We show how the constructed map can be used to increase global navigation safety by planning trajectories which avoid areas of high uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference on Humanoid Robots (Humanoids)