Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

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Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods $the use of multiple models (and filters) simultaneously - which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.

Survey of maneuvering target tracking. Part V. Multiple-model methods

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

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An Introduction To The Theory Of Point Processes

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.

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Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

A new method for multiple model particle filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running. The regularised particle filter probability density is a mixture of Gaussian probability densities. The proposed method is able to deal with nonlinearities and non-Gaussian noise. Furthermore, the new method keeps a fixed number of particles in each mode, and therefore it does not suffer from the potential drawbacks of existing multiple model particle filters for Markovian switching systems.

Interacting multiple model particle filter

The paper deals with a radar ‘track before detect’ application in a multitarget setting ‘Track before detect’ is a method to track weak objects on the basis of raw radar measurements, eg the reflected power of the target plus noise In classical target tracking, the tracking process is performed on the basis of pre-processed measurements that are constructed from the original measurement data every time step In this way no integration over time takes place and information is lost The authors give details of a modelling setup and a particle filter based algorithm to deal with a ‘multiple target track before detect’ situation Using simulations it is shown that with this method, it is possible to track multiple, closely spaced, weak targets

Multitarget particle filter track before detect application

For certain types of sensor-target configurations, a point target model or approach is not suitable and the physical extent of the target is accounted for during processing. An extended target track-before-detect (TBD) algorithm is presented and the performance is compared to an algorithm based on the point target assumption. Simulations illustrate the gain in performance obtained by using the extended target model where a particle filter is used for the TBD implementation.

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Track-before-detect algorithm for tracking extended targets

We present a new method to perform detection and tracking of a possible target in noise. We perform tracking not on the basis of the standard measurements but on the raw radar video data. Detection then is based upon the a posteriori information, i.e., the probability density of the state given these past measurements (in this case video data). This way of data processing/tracking is also referred to as track before detect (TBD) for obvious reasons. An advantage of this method over classical tracking is that in this TBD approach the decision whether a target is present or not is based on integrated and kinematically correlated energy. This method is better suited for tracking weak targets in noise than the classical method. As this problem statement leads to a nonlinear non-Gaussian filtering problem classical filtering methods (e.g. Kalman filtering) will result in poor performance. A particle filter is used to deal with the nonlinearities and the non-Gaussian nature of the noise. The same particle filter output is also used to perform detection based on a likelihood ratio test.

Particle filter based detection for tracking

An efficient particle filtering algorithm is presented for state estimation of jump Markov nonlinear systems. The standard particle filter can easily suffer from particle degeneracy around mode changes. The new algorithm allows the user to fully control the number of particles in a certain mode, without distorting or violating the Markov property. This property can be exploited to prevent the filter degenerating.

Yvo Boers

Papers

Interacting multiple model particle filter

Multitarget particle filter track before detect application

Track-before-detect algorithm for tracking extended targets

Particle filter based detection for tracking

Efficient particle filter for jump Markov nonlinear systems