Large Scale SLAM in an Urban Environment

Large Scale SLAM in an Urban Environment

Abstract

In robotics, the Simultaneous Localisation And Mapping (SLAM) problem consists of letting a robot map a previously unknown environment, while simultaneously localising the robot in the same map. In this thesis, an attempt to solve the SLAM problem in constant time in a complex environment, such as a suburban area, is made. Such a solution must handle increasing amounts of data without significant increase in computation time. A delayed state information filter is used to estimate the robot’s trajectory, and camera and laser range sensors are used to acquire spatial information about the environment along the trajectory. Two approaches to loop closure detection are proposed. The first is image based using Tree of Words for image comparison. The second is laser based using a trained classifier for laser scan comparison. The relative pose, the difference in position and heading, of two poses matched in loop closure is calculated with laser scan alignment using a combination of Conditional Random Field-Match and Iterative Closest Point. Experiments show that both image and laser based loop closure detection works well in a suburban area, and results in good estimation of the map as well as the robot’s trajectory.

Introduction

The simultaneous localisation and mapping (SLAM) problem is one of the most central in robotics research. It asks if a mobile robot, put in an unknown location in an unknown environment, can incrementally build a consistent map of the environment and simultaneously determine its location within this map. A general solution would enable truly autonomous robots. There are several areas in which autonomous vehicles of different sorts would be of great aid. Some examples are working in dangerous environments such as mines, herding of animals on remote farms with planes/ helicopters, inspection and monitoring of coral reefs with submarines and space excursions to other planets.

1.1 Problem Formulation

The mathematics behind the SLAM problem are today more or less fully explained [9]. Further, solutions to the SLAM problem in simplified laboratory settings have been suggested and proved functional. Among those are the Extended Kalman Filter solution, called EKF-SLAM, and the particle filter solution, called FastSLAM [29]. The properties of these two, and other suggested solutions, have been thoroughly examined. What remains is a solution to a larger scale problem, set outdoors in a more complex environment. In this thesis the SLAM problem was imagined to cover an area as large as 100 km2 of an urban environment. All elements of the solution must function equally well and fast in this scale as it would in a laboratory setting. The vehicle used in the experiments is a utility vehicle (UTE), Figure 1.1, equipped with a forward facing camera, one forward facing and one backwards facing laser sensor, wheel encoders, a steering wheel encoder and an inertial measurement unit (IMU).

Chapters: 1 - 5
Delivery: Email

Number of Pages: 75

Price: 5000 NGN

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