Istraživanje i modeliranje nepoznatog poligonalnog prostora zasnovano na nesigurnim podacima udaljenosti

We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman’s exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot’s pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman’s algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.Razmatramo problem istraživanja i izgradnje karte nepoznatog unutarnjeg prostora koristeći laserski sensor udaljenosti. Pretpostavljamo riješenu lokalizaciju robota i poznati model nesigurnosti senzora. Većna se algoritama istraživanja zasniva na otkrivanju granica istraženog i neistraženog područja. Međutim, u praksi nisu učinkoviti zbog nesigurnosti mjerenja, lokalizacije i izgradnje karte. Razvijen je algoritam istraživanja i izgradnje karte koji proširuje Ekmanov algoritam uklanjanjem strogih ograničenja na senzor udaljenosti i lokalizaciju robota. Razvijeni algoritam uključuje algoritam izdvajanja linijskih segmenata prema Pfisteru, koji uzima u obzir utjecaje zašumljenosti senzora i nesigurnosti položaja mobilnog robota. Linijska reprezentacija podataka udaljenosti koristi se za stvaranje poligona koji predstavlja istraženo područje iz svakog mjernog položaja. Bridovi poligona koji se ne podudaraju sa stvarnim značajkama prostora su kandidati za novi mjerni položaj. Algoritam općenitog isijecanja poligona korišten je za dobivanje ukupnog istraženog područja kao unija poligona iz različitih mjernih položaja. Razvijeni algoritam testiran je i uspoređen s izvornim Ekmanovim algoritmom simulacijski i eksperimentalno na mobilnom robotu Pioneer 3DX opremljenim laserskim senzorom udaljenosti SICK LMS-200

Curvature-Based Environment Description for Robot Navigation Using Laser Range Sensors

This work proposes a new feature detection and description approach for mobile robot navigation using 2D laser range sensors. The whole process consists of two main modules: a sensor data segmentation module and a feature detection and characterization module. The segmentation module is divided in two consecutive stages: First, the segmentation stage divides the laser scan into clusters of consecutive range readings using a distance-based criterion. Then, the second stage estimates the curvature function associated to each cluster and uses it to split it into a set of straight-line and curve segments. The curvature is calculated using a triangle-area representation where, contrary to previous approaches, the triangle side lengths at each range reading are adapted to the local variations of the laser scan, removing noise without missing relevant points. This representation remains unchanged in translation or rotation, and it is also robust against noise. Thus, it is able to provide the same segmentation results although the scene will be perceived from different viewpoints. Therefore, segmentation results are used to characterize the environment using line and curve segments, real and virtual corners and edges. Real scan data collected from different environments by using different platforms are used in the experiments in order to evaluate the proposed environment description algorithm

A comparison of line extraction algorithms using 2D range data for indoor mobile robotics

This paper presents an experimental evaluation of different line extraction algorithms applied to 2D laser scans for indoor environments. Six popular algorithms in mobile robotics and computer vision are selected and tested. Real scan data collected from two office environments by using different platforms are used in the experiments in order to evaluate the algorithms. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with ground truth using standard statistical methods. An extended case study is performed to further evaluate the algorithms in a SLAM applicatio

Concurrent Cognitive Mapping and Localization Using Expectation Maximization

Robot mapping remains one of the most challenging problems in robot programming. Most successful methods use some form of occupancy grid for representing a mapped region. An occupancy grid is a two dimensional array in which the array cells represents (x,y) coordinates of a cartesian map. This approach becomes problematic in mapping large environments as the map quickly becomes too large for processing and storage. Rather than storing the map as an occupancy grid, our robot (equipped with ultrasonic sonars) views the world as a series of connected spaces. These spaces are initially mapped as an occupancy grid in a room-by-room fashion using a modified version of the Histogram In Motion Mapping (HIMM) algorithm extended in this thesis. As the robot leaves a space, denoted by passing through a doorway, it converts the grid to a polygonal representation using a novel edge detection technique. Then, it stores the polygonal representation as rooms and hallways in a set of Absolute Space Representations (ASRs) representing the space connections. Using this representation makes navigation and localization easier for the robot to process. The system also performs localization on the simplified cognitive version of the map using an iterative method of estimating the maximum likelihood of the robot\u27s correct position. This is accomplished using the Expectation Maximization algorithm. Treating vector directions from the polygonal map as a Gaussian distribution, the Expectation Maximization algorithm is applied, for the first time, to find the most probable correct pose while using a cognitive mapping approach