TOA-based indoor localization and tracking with inaccurate floor plan map via MRMSC-PHD filter

This paper proposes a novel indoor localization scheme to jointly track a mobile device (MD) and update an inaccurate floor plan map using the time-of-arrival measured at multiple reference devices (RDs). By modeling the floor plan map as a collection of map features, the map and MD position can be jointly estimated via a multi-RD single-cluster probability hypothesis density (MSC-PHD) filter. Conventional MSC-PHD filters assume that each map feature generates at most one measurement for each RD. If single reflections of the detected signal are considered as measurements generated by map features, then higher-order reflections, which also carry information on the MD and map features, must be treated as clutter. The proposed scheme incorporates multiple reflections by treating them as virtual single reflections reflected from inaccurate map features and traces them to the corresponding virtual RDs (VRDs), referred to as a multi-reflection-incorporating MSC-PHD (MRMSC-PHD) filter. The complexity of using multiple reflection paths arises from the inaccuracy of the VRD location due to inaccuracy in the map features. Numerical results show that these multiple reflection paths can be modeled statistically as a Gaussian distribution. A computationally tractable implementation combining a new greedy partitioning scheme and a particle-Gaussian mixture filter is presented. A novel mapping error metric is then proposed to evaluate the estimated map's accuracy for plane surfaces. Simulation and experimental results show that our proposed MRMSC-PHD filter outperforms the existing MSC-PHD filters by up to 95% in terms of average localization and by up to 90% in terms of mapping accuracy

State space reparametrization for approximating nonlinear models in Bayesian state estimation

Recursive Bayesian state estimation is a powerful methodology which is useful for the integration of data about a process of interest while considering all the sources of uncertainty which are present in the observations and in modeling inaccuracies. However, in its general form it is intractable and approximations need to be made in order to use it in real life applications. The most widely used algorithm to perform recursive state estimation is the Kalman filter, which assumes that the probability distributions that it propagates are Gaussian and that the measurement and dynamical processes are linear. If these assumptions are satisfied, the Kalman filter is optimal. In most applications, however, this proves to be an oversimplification, due to which several techniques have arisen to handle model non-linearity and different types of distributions. In this thesis, a novel method for the estimation of distributions with nonlinear dynamical and measurement models is presented, which uses a reparametrization of the state space of the distributions in order to exploit the linear properties of the Kalman filter. This involves the mapping of the distribution into a different space, and a subsequent approximation as a Gaussian distribution. An analysis of the adequacy of this transformation is presented, which shows that it is a valid approach in a number of practically interesting filtering problems. The proposed approach is applied to the estimation of the state of Earth-orbiting objects, as it is a challenging estimation scenario which can benefit from the use of filter. Space situational awareness is increasingly important as near-Earth space becomes cluttered with satellites and debris. In this work, the sensors that are most commonly used to track objects in orbit, radars and telescopes, are modeled and a filter based on the previously discussed ideas is proposed. Finally, a multi-object estimation filter based on a recent estimation framework is presented which propagates high amounts of information while maintaining low computational complexity. This is important as there are many challenges to tracking large amounts of orbiting objects in a principled way using ground-based sensors, and naturally extends the single object filter described above to the multi-sensor, multi-object case

Group and extended target tracking with the probability hypothesis density filter

Multiple target tracking concerns the estimation of an unknown and time-varying number of objects (targets) as they dynamically evolve over time from a sequence of measurements obtained from sensors at discrete time intervals. In the Bayesian ltering framework the estimation problem incorporates natural phenomena such as false measurements and target birth/death. Though theoretically optimal, the generally intractable Bayesian lter requires suitable approximations. This thesis is particularly motivated by a rst-order moment approximation known as the Probability Hypothesis Density (PHD) lter. The emphasis in this thesis is on the further development of the PHD lter for handling more advanced target tracking problems, principally involving multiple group and extended targets. A group target is regarded as a collection of targets that share a common motion or characteristic, while an extended target is regarded as a target that potentially generates multiple measurements. The main contributions are the derivations of the PHD lter for multiple group and extended target tracking problems and their subsequent closed-form solutions. The proposed algorithms are applied in simulated scenarios and their estimate results demonstrate that accurate tracking performance is attainable for certain group/extended target tracking problems. The performance is further analysed with the use of suitable metrics.Engineering and Physical Sciences Research Council (EPSRC) Industrial CASE Award Studentshi

公共空間における移動サービスの実現に向けた知能化移動プラットフォームの開発

本研究は，自律移動パーソナルヴィークルによる多様な移動サービス研究を行うための移動プラットフォーム開発に関するものである．現在，多くの研究機関で自律移動システムの研究が行われているが，それが送迎サービス等の実用的な移動サービスアプリケーションの研究開発まで至った例は多くない．これは，それらの研究で利用されている市販の移動プラットフォームや研究用プラットフォームでは，移動サービスアプリケーションの研究開発が容易でないことが要因の一つとなっている．また個々のパーソナルヴィークルのロボット化技術やナビゲーション機能の研究成果が共有できていない面が有り，移動サービス研究に耐えうる移動プラットフォームが構築できていないことも一つの要因であると考えられる．本研究では，上記の問題を解決するため，様々な移動サービスアプリケーション開発が行え，様々な移動サービスに関する研究成果を利用することが可能な仕組みを持ち，さらに基本的なナビゲーション機能を備える「知能化移動プラットフォーム」のシステム構成を文献調査や事例研究により明らかにした．またそれに基づき実際に知能化移動プラットフォームを構築し，その有用性を実証するとともにその構築方法についても明示した．第二章では，知能化移動プラットフォームに求められるシステム要件を設定し，関連研究・文献調査(829件)・「つくばチャレンジ」などの実証実験の事例観察からシステム要件を満たす知能化移動プラットフォームのシステム構成を明らかにした．第三章では，提案したシステム構成について，背景で述べた問題点を解決する評価指標を設定し，関連する研究事例や市販の移動プラットフォームと比較することで優位性を示した．第四章では，提案したシステム構成に基づき，使用場面に応じた二つの知能化移動プラットフォームを開発した．一つ目として屋外での移動サービスを想定し，所属研究室でこれまで開発されてきた走行性能が高い電動カートをベースとしたプラットフォーム開発を行った．基本ナビゲーション機能には当研究室での共同研究成果を搭載した．動作検証を学内及びつくばロボット特区で行い約1㎞以上の自律走行能力を有していることを確認し，提案したシステム構成が有効であることを確認した．二つ目として屋内外でシームレスな移動サービスを想定して，屋内における移動性能を重視した車椅子ベースの知能化移動プラットフォームを開発した．上記と同様のコンセプトで開発し学内において同様の動作検証を行いその自律走行能力を確認した．第五章では，開発した知能化移動プラットフォームを用いた移動サービスに関する研究成果（文献11件）に関して，設定したシステム要件を満たす実装がどのようにそれらの研究実績に繋がった考察を行い，設定したシステム要件と提案したシステム構成が有効であることを確認した．本研究の成果は，様々な移動サービス研究に用いることのできる移動プラットフォームのシステム要件を明らかにし，その構築に有効なシステム構成を示したこと，および実際に移動プラットフォームを開発し，その有用性を実際に示したことである．またその開発過程で述べたハードウェア及びソフトウェアのそれぞれに関する実装そのものも有効な知見として述べた．上記の成果は，多くの研究機関が移動サービス研究に従事するにあたり移動プラットフォーム開発の指針とすることができ，今後，移動サービス実現に向けて該当分野の研究がより推進されることが期待できる．電気通信大学201

Novel methods for multi-target tracking with applications in sensor registration and fusion

Maintaining surveillance over vast volumes of space is an increasingly important capability for the defence industry. A clearer and more accurate picture of a surveillance region could be obtained through sensor fusion between a network of sensors. However, this accurate picture is dependent on the sensor registration being resolved. Any inaccuracies in sensor location or orientation can manifest themselves into the sensor measurements that are used in the fusion process, and lead to poor target tracking performance. Solutions previously proposed in the literature for the sensor registration problem have been based on a number of assumptions that do not always hold in practice, such as having a synchronous network and having small, static registration errors. This thesis will propose a number of solutions to resolving the sensor registration and sensor fusion problems jointly in an efficient manner. The assumptions made in previous works will be loosened or removed, making the solutions more applicable to problems that we are likely to see in practice. The proposed methods will be applied to both simulated data, and a segment of data taken from a live trial in the field

Sonar attentive underwater navigation in structured environment

One of the fundamental requirements of a persistently Autonomous Underwater Vehicle (AUV) is a robust navigation system. The success of most complex robotic tasks depends on the accuracy of a vehicle’s navigation system. In a basic form, an AUV estimates its position using an on-board navigation sensors through Dead-Reckoning (DR). However DR navigation systems tends to drift in the long run due to accumulated measurement errors. One way of mitigating this problem require the use of Simultaneous Localization and Mapping (SLAM) by concurrently mapping external environment features. The performance of a SLAM navigation system depends on the availability of enough good features in the environment. On the contrary, a typical underwater structured environment (harbour, pier or oilfield) has a limited amount of sonar features in a limited locations, hence exploitation of good features is a key for effective underwater SLAM. This thesis develops a novel attentive sonar line feature based SLAM framework that improves the performance of a SLAM navigation by steering a multibeam sonar sensor,which is mounted on a pan and tilt unit, towards feature-rich regions of the environment. A sonar salience map is generated at each vehicle pose to identify highly informative and stable regions of the environment. Results from a simulated test and real AUV experiment show an attentive SLAM performs better than a passive counterpart by repeatedly visiting good sonar landmarks

SLAM with single cluster PHD filters

Recent work by Mullane, Vo, and Adams has re-examined the probabilistic foundations of feature-based Simultaneous Localization and Mapping (SLAM), casting the problem in terms of filtering with random finite sets. Algorithms were developed based on Probability Hypothesis Density (PHD) filtering techniques that provided superior performance to leading feature-based SLAM algorithms in challenging measurement scenarios with high false alarm rates, high missed detection rates, and high levels of measurement noise. We investigate this approach further by considering a hierarchical point process, or single-cluster multi-object, model, where we consider the state to consist of a map of landmarks conditioned on a vehicle state. Using Finite Set Statistics, we are able to find tractable formulae to approximate the joint vehicle-landmark state based on a single Poisson multi-object assumption on the predicted density. We describe the single-cluster PHD filter and the practical implementation developed based on a particle-system representation of the vehicle state and a Gaussian mixture approximation of the map for each particle. Synthetic simulation results are presented to compare the novel algorithm against the previous PHD filter SLAM algorithm. Results presented indicate a superior performance in vehicle and map landmark localization, and comparable performance in landmark cardinality estimation