Sensors and Data in Mobile Robotics for Localisation

Introduction

Robot navigation is challenging. Leonard and Durrant-Whyte (2012) define it by three questions:

• •

  ”where am I?”,

• •

  ”where am I going?”, and

• •

  ”how should I get there?”

The first question is localisation: establishing the exact position and orientation of the robot within the frame of reference in its environment, and is the focus here. The robot may be navigating in static or dynamic environments, in indoor or outdoor environments and using static (pre-defined) path determination or dynamic path determination. Each of these variants requires different considerations. Gul, Rahiman, & Nazli Alhady (2019) provide a survey of the algorithms used for robot navigation. Effective navigation requires success in the four building blocks of navigation (Siegwart, Nourbakhsh, & Scaramuzza, 2011):

• 1.

  perception - the robot must be able to analyse its sensors data to extract meaningful knowledge;

• 2.

  localization - the robot must be able to calculate its position in the environment;

• 3.

  cognition - the robot must be able to determine how to navigate to its goals using the information from 1 and 2;

• 4.

  motion control - the robot must be able to modulate its movement to achieve the desired trajectory.

This survey focuses on 1 and 2 but also considers 3. It focuses on the sensor data used, how and where they are used and their respective advantages and disadvantages. The Background section outlines the different types of mobile robots and identifies the focus for this survey, and Sensors for Robotics describes robotics sensors, their use in robot navigation and where the main challenges lie for localisation, Solutions and Recommendations examines the literature on localisation for local and global localisation and indoor and outdoor robotics. Future Research Directions considers the most likely developments in localisation and the Conclusion provides an overview of the article.

Background

A key task for any autonomous system is acquiring knowledge about its environment. For mobile robot navigation, this is done by taking measurements using various sensors and then eliciting meaningful information from those measurements. Jones, Seiger, & Flynn, (1998) surveyed mobile robotics sensors. Many of these sensors are still used today (in enhanced forms) but new sensors and data have been introduced. The aim of this survey is not to merely catalogue all publications on robot localisation. Rather, it surveys a broad cross-section of contributions that provide the reader with good coverage and insight into the subject. It focuses on interesting and varied contributions from the last decade that use affordable, consumer-grade sensors which have progressed significantly.