Robot Localization: An Introduction

Abstract

Robot localization is the process of determining where a mobile robot is located with respect to its environment. Localization is one of the most fundamental competencies required by an autonomous robot as the knowledge of the robot's own location is an essential precursor to making decisions about future actions. In a typical robot localization scenario, a map of the environment is available and the robot is equipped with sensors that observe the environment as well as monitor its own motion. The localization problem then becomes one of estimating the robot position and orientation within the map using information gathered from these sensors. Robot localization techniques need to be able to deal with noisy observations and generate not only an estimate of the robot location but also a measure of the uncertainty of the location estimate. This article provides an introduction to estimation of theoretic solutions to the robot localization problem. It begins by discussing the mathematical models used to describe the robot motion and observations from the sensors. Two of the most common probabilistic techniques, the extended Kalman filter and the particle filter, that can be used to combine information from sensors to compute an estimate of the robot location are then discussed in detail and illustrated by simple examples. A brief summary of the large body of literature on robot localization is presented next. Appendices that present the essential mathematical background and alternative techniques are provided. The MATLAB code of the localization algorithms is also available.

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

“…Localization is one of the most fundamental competencies required by an autonomous system, as the knowledge of the vehicle's location is an essential precursor to take any decisions about future actions, whether planned or unplanned. In a typical localization situation, a map of the environment or world is available and the robot is equipped with sensors that sense and observe the environment as well as monitor the robot's motion [188,[198][199][200]. Hence, localization is that branch in autonomous system navigation, which deals with the study and application of the ability of a robot to localize itself in a map or plan.…”

“…A process of establishing the spatial relationship between the intelligent system and the stationary objects Localization is achieved using devices like Global Positioning Systems(GPS), odometric sensors, Inertial Measurement Units (IMU), etc. These sensors give the position information of the autonomous system, which can be used by the system to see where it is in the environment or the robot world [198,201,202]. Some important techniques of localization are listed below.…”

A Systematic Review on Fusion Techniques and Approaches Used in Applications

“…It is a fundamental issue for any kind of mobile robots. Normally, a robot observes its motion and environment through onboard sensors [38]. Techniques for robot localization deal with the estimation of the robot location and noisy observations [38].…”

“…Kalman filter (KF) is normally used in real time applications to fuse dynamic sensor information [46]. According to [38], KF and EKF are the most common techniques for robot localization, however, because KF or EKF are fusion methods which embrace iterative algorithms to handle non-linear and linear models, the convergence is always uncertain [47]. The desired state representation is based on the sensor input which is known and fixed upfront.…”

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

“…The selection of the best route for moving the mobile robot within the environment is done with the help of a navigation approach consisting of path-planning and object avoidance algorithms to generate real-time trajectories and avoid collisions [13]. The key factor of the navigation is localization that helps to optimize the working point of the robot within its surrounding environment [14]. The efficient control of the robot navigation largely relies on the creation of proper mapping of the environment that gives detailed information of the trajectory and obstacles within the environment [15].…”