We present a new approach to the cooperative localisation problem by applying the theory of minimum energy filtering. We consider the problem of estimating the pose of a group of mobile robots in an environment where robots can perceive fixed landmarks and neighbouring robots as well as share information with others over a communication channel. Whereas the vast majority of the existing literature applies some variant of a Kalman Filter, we derive a set of filter equations for the global state estimate based on the principle of minimum energy filtering. We show how the filter equations can be decoupled and the calculations distributed among the robots in the network without requiring a central processing node. Finally, we provide a demonstration of the filter's performance in simulation.
Australian National University（澳大利亚国立大学）；Defence Science and Technology Group（国防科学技术集团）
Collaborative Localisation has been studied extensively in recent years as a way to improve pose estimation of unmanned aerial vehicles in challenging environments. However little attention has been paid toward advancing the underlying filter design beyond standard Extended Kalman Filter-based approaches. In this paper, we detail a discrete-time collaborative localisation filter using the deterministic minimum-energy framework. The filter incorporates measurements from an inertial measurement unit and models the effects of sensor bias and gravitational acceleration. We present a simulation bas…
Australian National University（澳大利亚国立大学）；Defence Science and Technology Group（国防科学与技术集团）
Accurate localisation of unmanned aerial vehicles is vital for the next generation of automation tasks. This paper proposes a minimum energy filter for velocity-aided pose estimation on the extended special Euclidean group. The approach taken exploits the Lie-group symmetry of the problem to combine Inertial Measurement Unit (IMU) sensor output with landmark measurements into a robust and high performance state estimate. We propose an asynchronous discrete-time implementation to fuse high bandwidth IMU with low bandwidth discrete-time landmark measurements typical of real-world scenarios. The …
Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation
Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, UK（林肯自主系统中心（L-CAS），林肯大学，英国）；MAVLab, TU Delft, Netherlands（MAV实验室，代尔夫特理工大学，荷兰）；Robotics Systems Lab, ETH Zurich, Switzerland（机器人系统实验室，苏黎世联邦理工学院，瑞士）
The main goal of this project is that the basic EKF-based SLAM operation can be implemented sufficiently for estimating the state of the UGV that is operated in this real environment involving dynamic objects. Several problems in practical implementation of SLAM operation such as processing measurement data, removing bias measurement, extracting landmarks from the measurement data, pre-filtering extracted landmarks and data association in the observed landmarks are observed during the operation of EKF-based SLAM system . In addition, the comparison of EKF-based SLAM operation with dead reckoni…
University of Lincoln（林肯大学）；University of Oxford（牛津大学）；Örebro University（厄勒布鲁大学）
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deep-learned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantl…
In the context of self-driving vehicles there is strong competition between approaches based on visual localisation and LiDAR. While LiDAR provides important depth information, it is sparse in resolution and expensive. On the other hand, cameras are low-cost and recent developments in deep learning mean they can provide high localisation performance. However, several fundamental problems remain, particularly in the domain of uncertainty, where learning based approaches can be notoriously over-confident. Markov, or grid-based, localisation was an early solution to the localisation problem but f…
Distant Object Localisation from Noisy Image Segmentation Sequences
Research Council of Finland（芬兰研究理事会）；RCF Flagship Forest–Human–Machine Interplay—Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE)（RCF旗舰森林-人类-机器交互——构建韧性，重新定义价值网络和赋能有意义体验（UNITE））
Australian Center for Field Robotics（澳大利亚现场机器人中心）；the University of Sydney（悉尼大学）
Robust and persistent localisation is essential for ensuring the safe operation of autonomous vehicles. When operating in large and diverse urban driving environments, autonomous vehicles are frequently exposed to situations that violate the assumptions of algorithms, suffer from the failure of one or more sensors, or other events that lead to a loss of localisation. This paper proposes the use of a behavior tree framework that can monitor the performance of localisation health metrics and triggers intelligent responses such as sensor switching and loss recovery. The algorithm presented select…
University of Bristol（布里斯托大学）；The Hong Kong University of Science and Technology（香港科技大学）；Shandong University（山东大学）；Jilin University（吉林大学）；Shenzhen Sanhang Industrial Technology Research Institute（深圳三航工业技术研究院）
Mobile robots are becoming increasingly important both for individuals and industries. Mobile robotic technology is not only utilised by experts in this field but is also very popular among amateurs. However, implementing a mobile robot to perform tasks autonomously can be expensive because of the need for various types of sensors and the high price of robot platforms. Hence, in this paper we present a mobile robot localisation and navigation system which uses a LEGO ultrasonic sensor in an indoor map based on the LEGO MINDSTORM NXT. This provides an affordable and ready-to-use option for most…
KTH Royal Institute of Technology（KTH皇家理工学院）；University of Cyprus（塞浦路斯大学）；Aalto University（阿尔托大学）；Cyprus University of Technology（塞浦路斯理工大学）
We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy C…
Modular Robot and Landmark Localisation Using Relative Bearing Measurements
Department of Electrical and Electronic Engineering, University of Melbourne（墨尔本大学电子与电气工程系）；School of Engineering, The Australian National University（澳大利亚国立大学工程学院）
提出一种面向独立子系统的模块化非线性最小二乘滤波方法，通过最小二乘推导集成 CI 算法，并在机器人-地标相对方位定位仿真中与联合滤波器及低通信变体进行权衡比较。
