Eric Nettleton

Delayed and asequent data in decentralized sensing networks

This paper presents an exact solution to the delayed data problem for the information form of the Kalman filter, together with its application to decentralised sensing networks. To date, the most common method of handling delayed data in sensing networks has been to use a conservative time alignment of the observation data with the filter time. However, by accounting for the correlation between the late data and the filter over the delayed period, an exact solution is possible. The inclusion of this information correlation term adds little extra complexity, and may be applied in an information filter update stage which is associative. The delayed data algorithm can also be used to handle data that is asequent or out of order. The asequent data problem is presented in a simple recursive information filter form. The information filter equations presented in this paper are applied in a decentralised picture compilation problem. This involves multiple aircraft tracking multiple ground targets and the construction of a single common tactical picture.

The ANSER Project: Data Fusion Across Multiple Uninhabited Air Vehicles

The objective of the autonomous navigation and sensing experiment research (ANSER) project is to demonstrate decentralized data fusion (DDF) and simultaneous localization and map building (SLAM) across multiple uninhabited air vehicles (UAVs). To achieve this objective, the project specifies the development of four UAVs, where each UAV houses up to two terrain sensors and an INS/GPS navigation system. The terrain sensors include a scanning radar, laser/vision and standard vision system. The DDF concept has to be shown to be effective both on a single UAV and on multiple UAVs. The proof of the concept will lie in the ability of the DDF structure to conduct multi-target tracking problems as well as SLAM. To obtain this goal, a number of subgoals are required, most of which have never been attempted before on a research level. The objective of this paper is to present these goals as an overview of the ANSER project along with some simulated and real-time results.

Multiple-platform localization and map building

This paper presents current work on decentralized data fusion (DDF) applied to multiple unmanned aerial vehicles. The benefits of decentralizing algorithms, particularly in this field, are enormous. At a mission level, multiple aircraft may fly together sharing information with one another in order to produce more accurate and coherent estimates, and hence increase the chances of success. At the single platform level, algorithms may be decentralized throughout the airframe reducing the probability of catastrophic failure by eliminating the dependency on a particular central processing facility. To this end, a complex simulator has been developed to test and evaluate decentralized picture compilation, platform localization and simultaneous localization and map building (SLAM) algorithms which are to be implemented on multiple airborne vehicles. This simulator is both comprehensive and modular, enabling multiple platforms carrying multiple distributed sensors to be modeled and interchanged easily. The map building and navigation algorithms interface with both the simulator and the real airframe in exactly the same way in order to evaluate the actual flight code as comprehensively as possible. Logged flight data can also be played back through the simulator to the navigation routines instead of simulated sensors. This paper presents the structure of both the simulator and the algorithms that have been developed. An example of decentralized map building is included, and future work in decentralized navigation and SLAM systems is discussed.

Tracking in decentralised air-ground sensing networks

This paper describes the theoretical and practical development of a decentralised air and ground sensing network for target tracking and identification. The theoretical methods employed for studying decentralised data fusion problems are based on the information-filter formulation of the Kalman filter algorithm and on information-theoretic methods derived from the Bayes theorem. The paper particularly focuses on how these methods are applied in very large heterogeneous sensor networks, where there may be a significant amount of data delay or corruption in communication. This paper then describes the development of a practical system aimed at demonstrating some of these principles. The system consists of a number of unmanned air vehicles (UAVs), with radar and vision payloads, able to observe a number of ground targets. The UAV sensor payloads are constructed in a modular fashion, with the ability to communicate in a network with both other air-borne and other ground sensors. The ground sensor system comprises of multiple modular sensing nodes which include vision scanned laser, steerable radar, multiple fixed radar arrays, and combined night vision (IR)-radar.

Closed form solutions to the multiple-platform simultaneous localization and map building (SLAM) problem

This paper presents a closed form solution to the multiple platform simultaneous localization and map building (SLAM) problem. Closed form solutions are presented in both state space and information based forms. A key conclusion of this paper is that the information-state based form offers many advantages over the state space formulation in allowing the SLAM algorithm to be decentralized across multiple platforms.

On the linear and nonlinear observability analysis of the SLAM problem

Research in Simultaneous Localization and Mapping (SLAM) has been progressing for almost two decades. Although several researchers attempted recently to investigate its observability (mostly without proofs for the general cases) the established facts have often been left unnoticed or ignored by the research community. In this paper rigorous proofs have been provided as an enlightenment for the observability properties of the general two dimensional SLAM problem incorporating a car like kinematic model in the context of piece-wise constant systems theory and non-linear Lie derivative theory. Observable and Unobservable states of the general n landmark SLAM problem have been established with proofs. A comparison of linear and non-linear techniques to evaluate the observability of SLAM is provided using simulations.

Real time Multi-UAV Simulator

This paper presents the system architecture of a real time multi-UAV simulator (RMUS). The simulator has been implemented as both a testing and validation mechanism for the real demonstration of multiple UAVs conducting both decentralised data fusion and control. These mechanisms include the off-line simulation of complex scenarios, hardware-in-the-loop tests, validation of real test results, and online mission control system demonstrations. The paper also present CommLibX, a novel communication framework for the system which allows simulation modules to communicate over single or multiple virtual channels. This unique communication system is then easily ported onto the real hardware allowing for maximum reuse of software and integrity.

Decentralised ground target tracking with heterogeneous sensing nodes on multiple UAVs

This paper presents real time results of a decentralised air-borne data fusion system tracking multiple ground based targets. These target estimates are then used to construct a map of the environment. A decentralised communication strategy is employed which is robust to communication latencies and dropouts and results in each sensing node having a local estimate using global information. In addition, this paper describes both hardware and algorithms used to deploy two sensor nodes for such a task. Two sensor types will be discussed, vision and mm wave radar. The problems introduced by locating the sensors on air vehicles are both interesting and challenging. A total of four unmanned air vehicles will be employed to carry node payloads. Weight and power restrictions of the payloads coupled with the vehicle dynamics make the task of processing and fusing vision and radar based data a challenging problem indeed. This paper aims to highlight many of the problems that have been encountered in developing both hardware and software to operate under such constraints.

Heteroscedastic Gaussian processes for data fusion in large scale terrain modeling

This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain modeling for a mobile robot. Building a model of large scale and complex terrain that can adequately handle uncertainty and incompleteness in a statistically sound manner is a very challenging problem. To obtain a comprehensive model of such terrain, typically, multiple sensory modalities as well as multiple data sets are required. This work uses Gaussian processes to model large scale terrain. The model naturally provides a multi-resolution representation of space, incorporates and handles uncertainties appropriately and copes with incompleteness of sensory information. Gaussian process regression techniques are applied to estimate and interpolate (to fill gaps in unknown areas) elevation information across the field. In this work, the GP modeling approach is extended to fuse multiple, multi-modal data sets to obtain a best estimate of the elevation given the individual data sets. The individual data sets are treated as different noisy samples of the same underlying terrain. Experiments performed on sparse GPS based survey data and dense laser scanner data taken at mine-sites are reported.

Gaussian Process modeling of large scale terrain

This paper addresses the problem of large scale terrain modeling for a mobile robot. Building a model of large scale terrain data that can adequately handle uncertainty and incompleteness in a statistically sound way is a very challenging problem. This work proposes the use of Gaussian Processes as models of large scale terrain. The proposed model naturally provides a multi-resolution representation of space, incorporates and handles uncertainties aptly and copes with incompleteness of sensory information. Gaussian Process Regression techniques are applied to estimate and interpolate (to fill gaps in unknown areas) elevation information across the field. The estimates obtained are the best linear unbiased estimates for the data under consideration. A single Non-Stationary (Neural Network) Gaussian Process is shown to be powerful enough to model large and complex terrain, handling issues relating to discontinuous data effectively. A local approximation methodology based on KD-Trees is also proposed in order to ensure local smoothness and yet preserve the characteristic features of rich and complex terrain data. The use of the local approximation technique based on KD-Trees further addresses concerns relating to the scalability of the proposed approach for large data sets. Experiments performed on sparse GPS based survey data as well as dense laser scanner data taken at different mine-sites are reported in support of these claims.