29 accepted papers
Robotic systems with redundant degrees of freedom can achieve the same task outcome using multiple configurations, resulting in solution sets that form manifolds in the configuration space. Existing approaches typically exploit such redundancy locally through Jacobian-based techniques to compute ind…
General robot skill adaptation requires expressive representations robust to varying task configurations. While recent learning-based skill adaptation methods refined via Reinforcement learning (RL) have shown success, existing skill models often lack sufficient representational capacity for anythin
In this paper, we propose a robot skill-learning method that facilitates fast adaption to new tasks online. Our method is based on a hybrid learning from demonstration and reinforcement learning approach, which seeds learning with a compact and structured skill model, leading to efficient and stable…
Over the past decade, lidars have become a cornerstone of robotics state estimation and perception thanks to their ability to provide accurate geometric information about their surroundings in the form of 3D scans. Unfortunately, most of nowadays lidars do not take snapshots of the environment but s…
This work addresses the issue of motion compensation and pattern tracking in event camera data. An event camera generates asynchronous streams of events triggered independently by each of the pixels upon changes in the observed intensity. Providing great advantages in low-light and rapid-motion scen…
Learning from demonstration (LfD) has the potential to greatly increase the applicability of robotic manipulators in modern industrial applications. Recent progress in LfD methods have put more emphasis in learning robustness than in guiding the demonstration itself in order to improve robustness. T…
This paper presents an active visual-inertial mapping framework with points and planes. The key aspect of the proposed framework is a novel probabilistic plane extraction with its associated model for estimation. The approach allows the extraction of plane parameters and their uncertainties based on…
Robots reason about the environment through dedicated representations. Despite the fact that Gaussian Process (GP)-based representations are appealing due to their probabilistic and continuous nature, the cubic computational complexity is a concern. In this paper, we present a novel efficient GP-bas…
Keypoint annotation in pointclouds is an important task for 3D reconstruction, object tracking and alignment, in particular in deformable or moving scenes. In the context of agriculture robotics, it is a critical task for livestock automation to work toward condition assessment or behaviour recognit…
Trajectory prediction in a cluttered environment is key to many important robotics tasks such as autonomous navigation. However, there are an infinite number of possible trajectories to consider. To simplify the space of trajectories under consideration, we utilise homotopy classes to partition the…
In this paper, we introduce an event-based visual odometry and mapping framework that relies on decaying event-based corners. Event cameras, unlike conventional cam-eras, can provide sensor data during high-speed motions or in scenes with high dynamic ranges. Rather than providing intensity informat…
We present a planning framework for min-imising the deterministic worst-case error in sparse Gaus-sian process (GP) regression. We first derive a univer-sal worst-case error bound for sparse GP regression with bounded noise using interpolation theory on reproducing kernel Hilbert spaces (RKHSs). By…
In this paper, we present a novel approach that predicts spatially and temporally crowd behaviour for robotic social navigation. Integrating mobile robots into human society involves the fundamental problem of navigation in crowds. A robot should attempt to navigate in a way that is minimally invasi…
The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visua…
In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents adv…
Multi-modal estimation systems have the advantage of increased accuracy and robustness. To achieve accurate sensor fusion with these types of systems, a reliable extrinsic calibration between each sensor pair is critical. This paper presents a novel self-calibration framework for lidar-inertial syst…
Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust,…
In this paper, we introduce a probabilistic framework for INertial Lidar Localisation And MApping (IN2LAMA). Most of today's lidars are based on spinning mechanisms that do not capture snapshots of the environment. As a result, movement of the sensor can occur while scanning. Without a good estimati…
In this paper, we present a probabilistic framework to recover the extrinsic calibration parameters of a lidar-IMU sensing system. Unlike global-shutter cameras, lidars do not take single snapshots of the environment. Instead, lidars collect a succession of 3D-points generally grouped in scans. If t…
Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outl…
Detecting and tracking people is a key requirement in the development of robotic technologies intended to operate in human environments. In crowded environments such as train stations this task is particularly challenging due the high numbers of targets and frequent occlusions. In this paper we pres…
Building large-scale 2.5D maps when spatial correlations are considered can be quite expensive, but there are clear advantages when fusing data. While optimal submapping strategies have been explored previously in covariance-form using Gaussian Process for large-scale mapping, this paper focuses on…
Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. However, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informati…
In this paper, we present a method for real-time 3D sound sources mapping using an off-the-shelf robotic perception sensor equipped with a linear microphone array. Conventional approaches to map sound sources in 3D scenarios use dedicated 3D microphone arrays, as this type of arrays provide two degr…
This work exploits modeling spatial correlation in 2.5D data using Gaussian Processes (GPs), and produces constrained sampling realizations on these models to improve certainty in the predictions by means of integrating additional sparse information. Data organized in 2.5D such as elevation and thic…
In this paper, we propose a framework to map stationary sound sources while simultaneously localise a moving robot. Conventional methods for localisation and sound source mapping rely on a microphone array and either, 1) a proprioceptive sensor only (such as wheel odometry) or 2) an additional exter…
Typically 2.5D maps provide a compact and efficient representation of the environment. When sensor data is obtained from multiple sets of noisy measurements at differing resolutions, the problem of compounding this information together to provide an effective and efficient means of mapping is not tr…
An algorithm is proposed for matching data from different sensing modalities. The problem is formalised as a kidnapped robot problem, where Bayesian fusion is used to find the most likely location where both modalities agree. The key idea of our algorithm is to model the correlation between the two…
In this paper, an approach for sound source localisation and calibration of an asynchronous microphone array is proposed to be solved simultaneously. A graph-based Simultaneous Localisation and Mapping (SLAM) method is used for this purpose. Traditional sound source localisation using a microphone a…