Alastair Harrison

Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers

In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.

High quality 3D laser ranging under general vehicle motion

This paper describes an end-to-end system capable of generating high-quality 3D point clouds from the popular LMS200 laser on a continuously moving platform. We describe the hardware, data capture, calibration and data stream processing we have developed which yields remarkable detail in the generated point clouds of urban scenes. Given the increasing interest in outdoor 3D navigation and scene reconstruction by mobile platforms, our aim is to provide a level of hardware and algorithmic detail suitable for replication of our system by interested parties who do not wish to invest in dedicated 3D laser rangers.

Self-calibration for a 3D laser

In this paper we describe a method for the automatic self-calibration of a 3D laser sensor. We wish to acquire crisp point clouds and so we adopt a measure of crispness to capture point cloud quality. We then pose the calibration problem as the task of maximizing point cloud quality. Concretely, we use Rényi Quadratic Entropy to measure the degree of organization of a point cloud. By expressing this quantity as a function of key unknown system parameters, we are able to deduce a full calibration of the sensor via an online optimization. Beyond details on the sensor design itself, we fully describe the end-to-end intrinsic parameter calibration process and the estimation of the clock skews between the constituent microprocessors. We analyse performance using real and simulated data and demonstrate robust performance over 30 test sites.

Lost in translation (and rotation): Rapid extrinsic calibration for 2D and 3D LIDARs

This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.

TICSync: Knowing when things happened

Modern robotic systems are composed of many distributed processes sharing a common communications infrastructure. High bandwidth sensor data is often collected on one computer and served to many consumers. It is vital that every device on the network agrees on how time is measured. If not, sensor data may be at best inconsistent and at worst useless. Typical clocks in consumer grade PCs are highly inaccurate and temperature sensitive. We argue that traditional approaches to clock synchronization, such as the use of NTP are inappropriate in the robotics context. We present an extremely efficient algorithm for learning the mapping between distributed clocks, which typically achieves better than millisecond accuracy within just a few seconds. We also give a probabilistic analysis providing an upper-bound error estimate.

Image and Sparse Laser Fusion for Dense Scene Reconstruction

This paper is concerned with reconstructing the metric geometry of a scene imaged with a single camera and a scanning laser. Our aim is to assign each image pixel with a range value using both image appearance and sparse laser data. We pose the problem as an optimization of a cost function encapsulating a spatially varying smoothness cost and measurement compatibility. In particular we introduce a second order smoothness term. We derive cues for discontinuities in range from changes in image appearance and reflect this in the objective function.We show that our formulation distills down to solving a large linear system which can be solved swiftly using direct methods. Results are presented and analyzed using synthetic cases to demonstrate salient behaviours and on real data to highlight real-world applicability.

Automatic Self-calibration of a Full Field-of-View 3D n-Laser Scanner

This paper describes the design, build, automatic self-calibration and evaluation of a 3D Laser sensor using conventional parts. Our goal is to design a system which is an order of magnitude cheaper than commercial systems, with commensurate performance. In this paper we adopt point cloud ‘crispness’ as the measure of system performance that we wish to optimise. Concretely, we apply the information theoretic measure known as Renyi Quadratic Entropy to capture the degree of organisation of a point cloud. By expressing this quantity as a function of key unknown system parameters, we are able to deduce a full calibration of the sensor via an online optimisation. Beyond details on the sensor design itself, we fully describe the end-to-end extrinsic parameter calibration process, the estimation of the clock skews between the four constituent microprocessors and analyse the effect our spatial and temporal calibrations have on point cloud quality.

Describing, Navigating and Recognising Urban Spaces - Building an End-to-End SLAM System

This paper describes a body of work being undertaken by our research group aimed at extending the utility and reach of mobile navigation and mapping. Rather than dwell on SLAM estimation (which has received ample attention over past years), we examine sibling problems which remain central to the mobile autonomy agenda. We consider the problem detecting loop-closure from an extensible, appearance-based probabilistic view point and the use of visual geometry to impose topological constraints. We also consider issues concerning the intrinsic quality of 3D range data / maps and finally describe our progress towards substantially enhancing the semantic value of built maps through scene de-construction and labeling.

Continuous vehicle localisation using sparse 3D sensing, kernelised rényi distance and fast Gauss transforms

This paper is about estimating a smooth, continuous-time trajectory of a vehicle relative to a prior 3D laser map. We pose the estimation problem as that of finding a sequence of Catmull-Rom splines which optimise the Kernelised Rényi Distance (KRD) between the prior map and live measurements from a 3D laser sensor. Our approach treats the laser measurements as a continual stream of data from a smoothly moving vehicle. We side-step entirely the segmentation and feature matching problems incumbent in traditional point cloud matching algorithms, relying instead on a smooth and well behaved objective function. Importantly our approach admits the exploitation of sensors with modest sampling rates - sensors that take seconds to densely sample the workspace. We show how by appropriate use of the Improved Fast Gauss Transform we can reduce the order of the estimation problem from quadratic (straight forward application of the KRD) to linear. Although in this paper we use 3D laser, our approach is also applicable to vehicles using 2D laser sensing or dense stereo. We demonstrate and evaluate the performance of our approach when estimating the full 6DOF continuous time pose of a road vehicle undertaking over 2.7km of outdoor travel.