Visual Precis Generation using Coresets

Example of a coreset cluster. The coreset point is indicated in red. Highly similar views are clustered together indicating that the coreset is effective in removing redundancy in the data. Given an image stream, we demonstrate an on-line algorithm that will select the semantically-important images that summarize the visual experience of a mobile [...]


In the context of decision making in robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention to the ways in which it generates scores. Precision [...]

Knowing When We Don’t Know: Introspective Classification for Mission-Critical Decision Making

Classification precision and recall have been widely adopted by roboticists as canonical metrics to quantify the performance of learning algorithms. This paper advocates that for robotics applications, which often involve mission-critical decision making, good performance according to these standard metrics is desirable but insufficient to appropriately characterise system performance. We introduce and motivate the importance [...]

Can Priors Be Trusted? Learning to Anticipate Roadworks

Abstract—This paper addresses the question of how much a previously obtained map of a road environment should be trusted for vehicle localisation during autonomous driving by assessing the probability that roadworks are being traversed. We compare two formulations of a roadwork prior: one based on Gaussian Process (GP) classification and the other on a more [...]

How was your day? Online Visual Workspace Summaries using Incremental Clustering in Topic Space

Someday, mobile robots will operate continually. Day  after day, they will be in receipt of a  never ending stream of images. In anticipation of this, this paper is about having a mobile robot generate apt and compact summaries of its life experience. We consider a robot moving around its environment both revisiting and exploring, accruing [...]

Parsing Outdoor Scenes from Streamed 3D Laser Data Using Online Clustering and Incremental Belief Updates

Abstract - In this paper, we address the problem of continually parsing a stream of 3D point cloud data acquired from a laser sensor mounted on a road vehicle. We leverage an online star clustering algorithm coupled with an incre- mental belief update in an evolving undirected graphical model. The fusion of these techniques allows the [...]

Modelling Observation Correlations for Active Exploration and Robust Object Detection

Today, mobile robots are expected to carry out increasingly complex tasks in multifarious, real-world environments. Often, the tasks require a certain semantic understanding of the workspace. Consider, for example, spoken instructions from a human collaborator referring to objects of interest; the robot must be able to accurately detect these objects to correctly understand the instructions. [...]

Detection of Cars, Pedestrians and Bicyclists from 3D Point Clouds

This project provides an end-to-end system for the detection of cars, pedestrians and bicyclists -- hazardous objects that could potentially change their motion state, hence whose detection is key to successful autonomous driving. The video to the right shows typical system output. Each detected object is highlighted with a bounding box. Cars, pedestrians and bicyclists [...]