3D laser methods for calibrating and localising robotic vehicles

This thesis is about the construction and automatic target-less calibration of a 3D laser sensor; this is then used to localise an autonomous vehicle without using other sensors.
Two novel contributions to our knowledge of robotics are presented here. The first is an automatic calibration routine, which is capable of learning its calibration parameters using only data from a 3D laser scanner. Targets with known dimensions are not required, as has previously been the case. The second main contribution is a localisation algorithm, which uses the high quality data from the calibrated 3D laser scanner with trajectory information from an additional source to build maps of the environment. The vehicle subsequently localises itself within these maps, using the 3D laser sensor alone.
Inaccurate laser data manifests itself as blurring when it is plotted in 3D space. The automatic calibration routine recognises that the environment has a true underlying structure to it, and expresses the amount of disorder in the measured laser points using a cost function based on the entropy of the 3D laser data. By optimising this quantity, we obtain the true calibration parameters for the system. We have quantified the accuracy of this algorithm by simulating a static environment from which we draw laser measurements with known calibration parameters. It was found that our calibration system converges to the true calibration values of the sensor.
We also address the problem of robotic localisation, as a continuous problem, evaluating precisely the continuous trajectory that the robot has taken as well as the location of the robotic platform. Maps are constructed using the high accuracy data stream from the 3D laser, combining it with an odometry stream, to build high quality laser point cloud maps. The algorithm localises the robotic platform within these maps using a single 3D laser sensor. We vary our estimate of the vehicle's trajectory, treating the scans from the 3D laser and the location of the vehicle as continuous data streams, in a way that maximally aligns the 3D laser data and the map; this is achieved by optimising a cost function based on the Kernelised Rényi Distance. This procedure is typically computationally taxing; however, the computational complexity and computation time of the overall system have been reduced considerably using an efficient algorithm known as the Improved Fast Gauss Transform (IFGT), making the system viable even for large amounts of laser data. An additional speedup was achieved by calculating the Jacobian of the cost function, rearranging it to a form calculable using IFGT approximations. These efficiencies reduce the cost of evaluating the system to near real time.
We evaluate the accuracy of our localisation system by comparing it to a DGPS stream as the best available source of ground truth. We show that our system performs more consistently than DGPS. This was especially prominent in regions where the line of sight to GPS satellites was obscured by trees. It was found that the accuracy of our system was comparable to that of the DGPS system.

Keywords: robotics
Advisor: Newman, P