Perception answers the essential question of “What is around me?” Situational awareness is crucial for safe operation in real-world, dynamic environments.

In this research topic we examine how to equip machines with a semantic understanding of the world, how we can reliably recognise objects of interest across vast seasonal and environmental changes, and importantly, investigate how to augment the algorithms with an introspective capacity that is able to predict when they are uncertain, or may fail. For this we consider the interactions with localisation and mapping systems in what we call the navigation-perception loop, which can lead to workspace-specific experts.

As mobile robotics spans many domains, we consider multiple modalities for perception including cameras, lasers, radars, and combinations thereof.

Our latest research

Visual Precis Generation using Coresets

Given an image stream, we demonstrate an on-line algorithm that will select the semantically-important images that summarize the visual experience ...
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In the context of decision making in robotics, the use of a classification framework which produces scores with inappropriate confidences ...
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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 ...
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Model-Free Dynamic Object Detection and Tracking with 2D Lidar

This project aims at detecting and tracking moving objects with a 2D laser scanner independent of their classes and shapes. In this ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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Choosing Where To Go: Complete 3D Exploration With Stereo

This paper is about the autonomous acquisition of detailed 3D maps of a-priori unknown environments using a stereo camera - ...
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Hidden View Synthesis using Real-Time Visual SLAM for Simplifying Video Surveillance Analysis

Abstract— Understanding and analysing static or mobile surveillance cameras often requires knowledge of the scene and the camera placement. In ...
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Risky Planning: Path Planning over Costmaps with a Probabilistically Bounded Speed-Accuracy Tradeoff

Abstract— This paper is about generating plans over uncertain maps quickly. Our approach combines the ALT (A* search, landmarks and ...
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Self Help: Seeking Out Perplexing Images for Ever Improving Navigation

Abstract—This paper is a demonstration of how a robot can, through introspection and then targeted data retrieval, improve its own ...
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Planning to Perceive: Exploiting Mobility For Robust Object Detection

Abstract - Consider the task of a mobile robot autonomously navigating through an environment while detecting and mapping objects of interest ...
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Non-parametric Learning for Natural Plan Generation

We present a novel way to learn sampling distributions for sampling-based motion planners by making use of expert data. We ...
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Planning Most-Likely Paths from Overhead Imagery

In this work, we are concerned with planning paths from overhead imagery. The novelty here lies in taking explicit account of uncertainty in ...
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Semantic Categorization of Outdoor Scenes with Uncertainty Estimates using Multi-Class Gaussian Process Classification

Abstract— This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multi-class Gaussian Process (GP) ...
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Teaching a Randomized Planner to plan with Semantic fields

 Abstract—This paper presents a novel way to bias the sampling domain of stochastic planners by learning from example plans. We ...
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Using Text-Spotting to Query the World

Abstract—The world we live in is labeled extensively for the benefit of humans. Yet, to date, robots have made little ...
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