Global LIDAR Localization Localization using LIDAR has advantages over visual localization. In particular LIDAR has a great degree of viewpoint and lighting invariance. It is however less informative. We have developed machine learning algorithms to more reliably detect places using segments as a basic unit within point cloud. Efficient Segmentation and Mapping (ESM, RA-L 2019) [...]
Abstract—Field robotics applications have some unique and unusual data requirements -- the curating, organisation and management of which are often overlooked. An emerging theme is the use of large corpora of spatiotemporally indexed sensor data which must be searched and leveraged both offline and online. Increasingly we build systems that must never stop learning. Every [...]
Successful approaches to autonomous vehicle localisation and navigation typically involve 3D LIDAR scanners and a static, curated 3D map, both of which are expensive to acquire and maintain. We propose an experience-based approach to matching a local 3D swathe built using a push-broom 2D LIDAR to a number of prior 3D maps, each of which [...]
Abstract—We outline an approach for using concentration inequalities to perform rapid approximate multi-hypothesis testing. In a scenario where multiple hypotheses are ranked according to a large set of features, our scheme improves the efficiency of selecting the best hypothesis by providing a “bail-out threshold” at which unpromising hypotheses can be excluded from further evaluation. We [...]
Abstract - This paper concerns the creation of efficient surface representations from laser point clouds created by a push broom laser system. We produce a continuous, implicit, non-parametric and non-stationary representation with an update time that is constant. This allows us to form predictions of the underlying workspace surfaces at arbitrary locations and densities. The algorithm [...]
This paper is about the automated discovery and mapping of surfaces using a stereo pair. We begin with the observation that for any workspace which is topologically connected (i.e. does not contain free flying islands) there exists a single surface that covers the entirety of the workspace. We call this surface the covering surface. We [...]
Abstract—This paper concerns the creation of efficient surface representations from laser point clouds. We produce a continuous, implicit, non-parametric representation with an update time that is constant. The algorithm places no restriction on the complexity of the underlying workspace surfaces and automatically prunes redundant data via an information theoretic criterion. This criterion makes the use [...]
Abstract— This paper describes a probabilistic framework for appearance based navigation and mapping using spatial and visual appearance data. Like much recent work on appearance based navigation we adopt a bag-of-words approach in which positive or negative observations of visual words in a scene are used to discriminate between already visited and new places. [...]
Abstract—This paper is concerned with generating a continuous implicit representation of a robot’s workspace using sparse point cloud data. We adopt a Gaussian Process (GP) framework to model the underlying workspace surfaces and suggest a non- parametric formulation which allows us to capture the non-functional relation between ground plane and elevation that is not possible [...]
Abstract— This paper describes a probabilistic framework for appearance based navigation and mapping using spatial and visual appearance data. Like much recent work on appearance based navigation we adopt a bag-of-words approach in which positive or negative observations of visual words in a scene are used to discriminate between already visited and new places. In [...]
