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Artificial Intelligence

A SLAM system based on Hidden Markov Models

Profile image of Jesus SavageJesus Savage

2021, Informatics and Automation

https://doi.org/10.15622/IA.2022.21.7
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32 pages

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Abstract

We present a graph SLAM system based on Hidden Markov Models (HMM) where the sensor readings are represented with different symbols using a number of clustering techniques; then, the symbols are fused as a single prediction, to improve the accuracy rate, using a Dual HMM. Our system’s versatility allows to work with different types of sensors or fusion of sensors, and to implement, either active or passive, graph SLAM. The Toyota HSR (Human Support Robot) robot was used to generate the data set in both real and simulated competition environments. We tested our system in the kidnapped robot problem by training a representation, improving it online, and, finally, solving the SLAM problem.

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C. Stachniss

IEEE Intelligent Transportation Systems Magazine, 2010

Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.

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Toward SLAM on Graphs
Noah Cowan

Springer Tracts in Advanced Robotics, 2009

We present an algorithm for SLAM on planar graphs. We assume that a robot moves from node to node on the graph using odometry to measure the distance between consecutive landmark observations. At each node, the robot follows a branch chosen at random, without reporting which branch it follows. A low-level process detects (with some uncertainty) the presence of landmarks, such as corners, branches, and bumps, but only triggers a binary flag for landmark detection (i.e., the robot is oblivious to the details or "appearance" of the landmark). Under uncertainties of the robot's odometry, landmark detection, and the current landmark position of the robot, we present an E-M-based SLAM algorithm for two cases: (1) known, arbitrary topology with unknown edge lengths and (2) unknown topology, but restricted to "elementary" 1-and 2-cycle graphs. In the latter case, the algorithm (flexibly and reversibly) closes loops and allows for dynamic environments (adding and deleting nodes).

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LPG-SLAM: a Light-weight Probabilistic Graph-based SLAM
Kathia Melbouci

Le Centre pour la Communication Scientifique Directe - HAL - Université de Nantes, 2020

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Applying Graph-based SLAM Algorithm in a Simulated Environment
Sofianita (Dr) Mutalib

IOP Conference Series: Materials Science and Engineering, 2020

This paper explores the capabilities of a graph optimization-based Simultaneous Localization and Mapping (SLAM) algorithm known as Cartographer in a simulated environment. SLAM refers to the problem in which an agent attempts to determine its location in the immediate environment as well as constructing the map(s) of its environment. SLAM is one of the most important aspects in the implementation of autonomous vehicle. In this paper, we explore the capabilites of the Cartographer algorithm which is based on the newer graph optimization approach in improving SLAM problems. A series of experiments were tested in order to discover its Cartographer capabilities in tackling SLAM problems. Then, we compare the results of Cartographer with Hector SLAM, another graph-based SLAM algorithm. We present the results from the experiments which show some promising findings based on the amount of computer resources used and the quality of the map(s) produced.

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A comparison of SLAM algorithms based on a graph of relations
G. Grisetti

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches in the sense that it considers the energy needed to deform the trajectory estimated by a SLAM approach to the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations. We believe that our benchmarking framework allows the user an easy analysis and objective comparisons between different SLAM approaches.

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