Faculty of Engineering and Information Technologies, School of Aerospace, Mechanical and Mechatronic Engineering
Abstract
This thesis considers perception systems for urban environments. It focuses on the task of tracking dynamic objects and in particular on methods that can maintain the identities of targets through periods of ambiguity. Examples of such ambiguous situations occur when targets interact with each other, or when they are occluded by other objects or the environment. With the development of self driving cars, the push for autonomous delivery of packages, and an increasing use of technology for security, surveillance and public-safety applications, robust perception in crowded urban spaces is more important than ever before. A critical part of perception systems is the ability to understand the motion of objects in a scene. Tracking strategies that merge closely-spaced targets together into groups have been shown to offer improved robustness, but in doing so sacrifice the concept of target identity. Additionally, the primary sensor used for the tracking task may not provide the information required to reason about the identity of individual objects. There are three primary contributions in this work. The first is the development of 3D lidar tracking methods with improved ability to track closely-spaced targets and that can determine when target identities have become ambiguous. Secondly, this thesis defines appearance models suitable for the task of determining the identities of previously-observed targets, which may include the use of data from additional sensing modalities. The final contribution of this work is the combination of lidar tracking and appearance modelling, to enable the clarification of target identities in the presence of ambiguities caused by scene complexity. The algorithms presented in this work are validated on both carefully controlled and unconstrained datasets. The experiments show that in complex dynamic scenes with interacting targets, the proposed methods achieve significant improvements in tracking performance
