The application of the extended Kaman filter to the problem of mobile robot navigation in a known environment is presented. An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed. The algorithm is based on an extended Kalman filter that utilizes matches between observed geometric beacons and an a priori map of beacon locations. Two implementations of this navigation algorithm, both of which use sonar, are described. The first implementation uses a simple vehicle with point kinematics equipped with a single rotating sonar. The second implementation uses a 'Robuter' mobile robot and six static sonar transducers to provide localization information while the vehicle moves at typical speeds of 30 cm/s. >

Mobile robot localization by tracking geometric beacons

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can "switch" from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently-between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations.

Interacting multiple model methods in target tracking: a survey

Interacting Multiple Model Methods in Target Tracking: A Survey

List of Figures. List of Tables. List of Abbreviations and Symbols. Preface. 1. Introduction. 2. A Sonar Sensor Model. 3. Model-Based Localization. 4. Map Building. 5. Simultaneous Map Building and Localization. 6. Directed Sensing Strategies. 7. Why Use Sonar? A. Hardware and Software. Bibliography. Index.

Directed Sonar Sensing for Mobile Robot Navigation

Various multisensor network scenarios with signal processing tasks that are amenable to multiprocessor implementation are described The natural origins of such multitasking are emphasized, and novel parallel structures for state estimation using the Kalman filter are proposed that extend existing results in several directions In particular, hierarchical network structures are developed that have the property that the optimal global estimate based on all the available information can be reconstructed from estimates computed by local processor nodes solely on the basis of their own local information and transmitted to a central processor The algorithms potentially yield an approximately linear speedup rate, are reasonably failure-resistant, and are optimized with respect to communication bandwidth and memory requirements at the various processors >

Decentralized structures for parallel Kalman filtering

The purpose of this paper is to examine several Kalman filter algorithms that can be used for state estimation with a multiple sensor system. In a synchronous data collection system, the statistically independent data blocks can be processed in parallel or sequentially, or similar data can be compressed before processing; in the linear case these three filter types are optimum and their results are identical. When measurements from each sensor are statistically independent, the data compression method is shown to be computationally most efficient, followed by the sequential processing; the parallel processing is least efficient.

D. Willner

Papers

Kalman filter algorithms for a multi-sensor system