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Bernoulli Filter for Track-Before-Detect: Swerling-1 Target in K-distributed Clutter

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Bernoulli Filter for Track-Before-Detect: Swerling-1 Target ...

IEEE International Conference on Radar

作者： Branko Ristic Luke Rosenberg Du Yong Kim Xuezhi Wang Jason Williams School of Engineering RMIT University Australia Defence Science and Technology Group Australia Robotics and Autonomous Systems Group Data61 CSIRO Australia

ISBN: (数字)9781728126609

ISBN: (纸本)9781728137858

In this paper, we investigate the problem of detecting and tracking small targets in sea clutter using the Bernoulli Track-Before-Detect filter. This filter is an optimal recursive Bayesian detector / estimator to determine the state of a single target and its presence in noise. Typically, clutter amplitude fluctuations are modelled using the Rayleigh distribution. This assumption, however, is not valid in the maritime domain, where sea-clutter is often spikier with a longer distribution tail. In this work, we develop the Bernoulli filter for a compound Gaussian clutter model with thermal noise and model the target with a Swerling 1 fluctuation. To demonstrate the performance improvement, we model the texture as a gamma distribution, giving an overall K plus noise distribution for the clutter plus noise. The detection and tracking improvement is then demonstrated using Monte Carlo simulation.

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Synthetic human model dataset for skeleton driven non-rigid motion tracking and 3d reconstruction

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arXiv 2019年

作者： Elanattil, Shafeeq Moghadam, Peyman Robotics and Autonomous Systems CSIRO Data61 Brisbane Australia Queensland University of Technology Brisbane Australia

We introduce a synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras. The dataset consist of seven motion sequences of a single human model. For each motion sequence per-frame ground truth geometry and ground truth skeleton are given. The dataset also contains skinning weights of the human model. More information about the dataset can be found at: Https://***/robotics/ our-work/databases/synthetic-human-model-dataset/. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion analysis

A Comparative Study of Track-Before-Detect Algorithms in Radar Sea Clutter

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A Comparative Study of Track-Before-Detect Algorithms in Rad...

IEEE International Conference on Radar

作者： Du Yong Kim Branko Ristic Xuezhi Wang Luke Rosenberg Jason Williams Sam Davey School of Engineering RMIT University Australia Defence Science and Technology Group Australia Robotics and Autonomous Systems Group Data61 CSIRO Australia

ISBN: (数字)9781728126609

ISBN: (纸本)9781728137858

The detection and tracking of targets in the maritime environment can be a challenging problem. One approach to improve the tracking ability of weak targets is to use track before detect (TBD). However, different TBD algorithms have different underlying assumptions about the background clutter which are not always valid. In this paper we consider three TBD algorithms: the histogram probabilistic multi-hypothesis tracker (H-PMHT), the Bernoulli filter and the multi-Bernoulli filter. The H-PMHT inherently assumes the clutter is modelled with a distribution derived from the continuous limit of the Poisson distribution, while the two Bernoulli filters both assume Rayleigh distributed clutter. To quantify performance, we use a Monte Carlo simulation with the sea clutter generated using both Rayleigh and K-distributions. The performance is then determined using the optimal sub-pattern assignment (OSPA) distance.

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Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

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arXiv 2021年

作者： Huang, Qiangqiang Pu, Can Khosoussi, Kasra Rosen, David M. Fourie, Dehann How, Jonathan P. Leonard, John J. MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States MIT Department of Nuclear Science and Engineering CambridgeMA02139 United States Robotics and Autonomous Systems Group DATA61 CSIRO BrisbaneQLD4069 Australia Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States NavAbility BostonMA02110 United States MIT Department of Aeronautical and Astronautical Engineering CambridgeMA02139 United States

This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging non-Gaussian setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association). Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

Robust photogeometric localization over time for map-centric loop closure

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arXiv 2019年

作者： Park, Chanoh Kim, Soohwan Moghadam, Peyman Guo, Jiadong Sridharan, Sridha Fookes, Clinton Robotics and Autonomous Systems Group DATA61 CSIRO BrisbaneQLD4069 Australia Brisbane Australia Division of Smart Automotive Engineering Sun Moon University Korea Republic of Autonomous Systems Lab ETH Zurich Switzerland

—Map-centric SLAM is emerging as an alternative of conventional graph-based SLAM for its accuracy and efficiency in long-term mapping problems. However, in map-centric SLAM, the process of loop closure differs from that of conventional SLAM and the result of incorrect loop closure is more destructive and is not reversible. In this paper, we present a tightly coupled photogeometric metric localization for the loop closure problem in map-centric SLAM. In particular, our method combines complementary constraints from LiDAR and camera sensors, and validates loop closure candidates with sequential observations. The proposed method provides a visual evidence-based outlier rejection where failures caused by either place recognition or localization outliers can be effectively removed. We demonstrate the proposed method is not only more accurate than the conventional global ICP methods but is also robust to incorrect initial pose guesses. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

Evolving spiking neural networks for nonlinear control problems

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arXiv 2019年

作者： Qiu, Huanneng Garratt, Matthew Howard, David Anavatti, Sreenatha School of Engineering and Information Technology University of New South Wales Australian Defence Force Academy Canberra Australia Robotics and Autonomous Systems Group DATA61 at CSIRO Brisbane Australia

Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons. Most of the research employing spiking neurons has been non-behavioural and discontinuous. Comparatively, this paper presents a recurrent spiking controller that is capable of solving nonlinear control problems in continuous domains using a popular topology evolution algorithm as the learning mechanism. We propose two mechanisms necessary to the decoding of continuous signals from discrete spike transmission: (i) a background current component to maintain frequency sufficiency for spike rate decoding, and (ii) a general network structure that derives strength from topology evolution. We demonstrate that the proposed spiking controller can learn significantly faster to discover functional solutions than sigmoidal neural networks in solving a classic nonlinear control problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

OVPC Mesh: 3D Free-space Representation for Local Ground Vehicle Navigation

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arXiv 2018年

作者： Ruetz, Fabio Hernández, Emili Pfeiffer, Mark Oleynikova, Helen Cox, Mark Lowe, Thomas Borges, Paulo Autonomous Systems Lab ETH Zurich Switzerland Robotics and Autonomous Systems Data61 CSIRO Brisbane Australia

— This paper presents a novel approach for local 3D environment representation for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point Clouds Mesh (OVPC Mesh). Our approach represents the surrounding of the robot as a watertight 3D mesh generated from local point cloud data in order to represent the free space surrounding the robot. It is a conservative estimation of the free space and provides a desirable trade-off between representation precision and computational efficiency, without having to discretize the environment into a fixed grid size. Our experiments analyze the usability of the approach for UGV navigation in rough terrain, both in simulation and in a fully integrated real-world system. Additionally, we compare our approach to well-known state-of-the-art solutions, such as Octomap and Elevation Mapping and show that OVPC Mesh can provide reliable 3D information for trajectory planning while fulfilling real-time constraints. Copyright © 2018, The Authors. All rights reserved.

关键词： Mesh generation

Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction

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arXiv 2018年

作者： Elanattil, Shafeeq Moghadam, Peyman Denman, Simon Sridharan, Sridha Fookes, Clinton Robotics and Autonomous Systems CSIRO DATA61 BrisbaneQLD4069 Australia Brisbane Australia

This paper presents a method which can track and 3D reconstruct the non-rigid surface motion of human performance using a moving RGB-D camera. 3D reconstruction of marker-less human performance is a challenging problem due to the large range of articulated motions and considerable non-rigid deformations. Current approaches use local optimization for tracking. These methods need many iterations to converge and may get stuck in local minima during sudden articulated movements. We propose a puppet model-based tracking approach using skeleton prior, which provides a better initialization for tracking articulated movements. The proposed approach uses an aligned puppet model to estimate correct correspondences for human performance capture. We also contribute a synthetic dataset which provides ground truth locations for frame-by-frame geometry and skeleton joints of human subjects. Experimental results show that our approach is more robust when faced with sudden articulated motions, and provides better 3D reconstruction compared to the existing state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Musculoskeletal system

Building an Aerial-Ground robotics System for Precision Farming: An Adaptable Solution

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arXiv 2019年

作者： Pretto, Alberto Aravecchia, Stéphanie Burgard, Wolfram Chebrolu, Nived Dornhege, Christian Falck, Tillmann Fleckenstein, Freya Fontenla, Alessandra Imperoli, Marco Khanna, Raghav Liebisch, Frank Lottes, Philipp Milioto, Andres Nardi, Daniele Nardi, Sandro Pfeifer, Johannes Popović, Marija Potena, Ciro Pradalier, Cédric Rothacker-Feder, Elisa Sa, Inkyu Schaefer, Alexander Siegwart, Roland Stachniss, Cyrill Walter, Achim Winterhalter, Wera Wu, Xiaolong Nieto, Juan Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy International Research Lab 2958 Georgia Tech-CNRS Metz France Department of Computer Science University of Freiburg Germany Toyota Research Institute Los Altos United States Photogrammetry & Robotics Lab University of Bonn Germany Robert Bosch GmbH Corporate Research Renningen Germany Osimo Italy Autonomous Systems Lab. Department of Mechanical and Process Engineering ETH Zurich Zürich Switzerland Department of Environmental Systems Science Institute of Agricultural Sciences ETH Zurich Zürich Switzerland IT+Robotics Srl Padova Italy Smart Robotics Lab. Department of Computing Imperial College London London United Kingdom CSIRO Data61 Australia

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping