This paper presents a robust 3D position tracking and map generation algorithm that combines data from a Laser Rangefinder and a RGB-D camera. Our algorithm is capable of running in real time on resource constrained robots. The novelty of this paper is the fusing of 2D-ICP from a Laser Rangefinder directly into a 3D position tracking, map building and SLAM system. Our approach takes advantage of the wide field of view and precision of laser scans to significantly improve the efficiency and accuracy of 3D alignment. We introduce a regularisation term to restrict movement in degrees of freedom that are not constrained by surface geometry. With efficiency in mind, we mirror a 2D local map based Graph SLAM approach in 3D to reliably detect loops if the robot comes near to an area it has previously visited. The mesh of each local map is warped after a graph of local maps is optimised to maintain a globally fused map. We provide experimental results to show that our algorithm is able to generate dense, globally consistent, mesh-based maps on resource constrained robots.

The efficient use of computing resources on a heterogeneous robotics platform, both in terms of run time performance and power usage, presents an interesting research problem, and is the focus of my research. It is envisaged that this will be achieved by both finding parallel approaches to algorithms commonly used in robotics, and investigating the use of a scheduler to efficiently allocate resources across a heterogeneous hardware platform. In particular, while there has been much research on using specialized hardware for image and video processing algorithms, work on areas specific to robotics, such as position tracking, mapping and sensor fusion, is not as common.

Learning a map of an unknown environment and localising a robot in it is a common problem in robotics, with solutions usually requiring an estimate of the robot's motion. In scenarios such as Urban Search and Rescue, motion encoders can be highly inaccurate, and weight and battery requirements often limit computing power. We have developed a GPU based algorithm using Iterative Closest Point position tracking and Graph SLAM that can accurately generate a map of an unknown environment without the need for motion encoders and requiring minimal computational resources. The algorithm is able to correct for drift in the position tracking by rapidly identifying loops and optimising the map. We present a method for refining the existing map when revisiting areas to increase the accuracy of the existing map and bound the run-time to the size of the environment.

Advances in sensing technology and algorithm design make it possible for a robot equipped with a laser range-finder to generate a map and localise itself within the map as the robot explores its environment.We describe a system for mapping and virtual reconstruction developed as part of a robot for urban search and rescue. The process of mapping and, at the same time, localising the robot within the map, is called Simultaneous Localisation and Mapping (SLAM). In many applications, such as urban search and rescue, information from wheel encoders is inaccurate and cannot be used for odometry to obtain a position estimate. However, iterative closest point scan matching algorithms make it possible for a robot to perform accurate positioning in unstructured environments where wheel slip is common. When this positioning is combined with a mapping algorithm such as FastSLAM, the robot can construct an accurate map in real-time as it moves. Given the generated map and the robot's position within it, a variety of exploration algorithms allow the robot to autonomously explore its environment. The robot is also equipped with an RGB-D camera. The 3D information as well as the colour video images are incorporated into the map to produce a 3D virtual reconstruction of the environment as the robot explores. This robot won the award for best autonomous robot in three successive RoboCup Rescue Robot competitions [1], 2009 - 2011.