Juan I. Nieto

Consistency of the FastSLAM algorithm

This paper presents an analysis of FastSLAM - a Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping. It shows that the algorithm degenerates with time, regardless of the number of particles used or the density of landmarks within the environment, and would always produce optimistic estimates of uncertainty in the long-term. In essence, FastSLAM behaves like a non-optimal local search algorithm; in the short-term it may produce consistent uncertainty estimates but, in the long-term, it is unable to adequately explore the state-space to be a reasonable Bayesian estimator. However, the number of particles and landmarks does affect the accuracy of the estimated mean and, given sufficient particles, FastSLAM can produce good non-stochastic estimates in practice. FastSLAM also has several practical advantages, particularly with regard to data association, and would probably work well in combination with other versions of stochastic SLAM, such as EKF-based SLAM

Recursive scan-matching SLAM

This paper presents Scan-SLAM, a new generalization of simultaneous localization and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage of EKF-SLAM and scan correlation. Landmarks are no longer defined by analytical models; instead they are defined by templates composed of raw sensed data. These templates can be augmented as more data become available so that the landmark definition improves with time. A new generic observation model is derived that is generated by scan correlation, and this permits stochastic location estimation for landmarks with arbitrary shape within the Kalman filter framework. The statistical advantages of an EKF representation are augmented with the general applicability of scan matching. Scan matching also serves to enhance data association reliability by providing a shape metric for landmark disambiguation. Experimental results in an outdoor environment are presented which validate the algorithm.

Real time data association for FastSLAM

The ability to simultaneously localise a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. This paper presents a real-world implementation of FastSLAM, an algorithm that recursively estimates the full posterior distribution of both robot pose and landmark locations. In particular, we present an extension to FastSLAM that addresses the data association problem using a nearest neighbor technique. Building on this, we also present a novel multiple hypotheses tracking implementation (MHT) to handle uncertainty in the data association. Finally an extension to the multi-robot case is introduced. Our algorithm has been run successfully using a number of data sets obtained in outdoor environments. Experimental results are presented that demonstrate the performance of the algorithms when compared with standard Kalman filter-based approaches.

Robust Inference of Principal Road Paths for Intelligent Transportation Systems

Over the last few years, electronic vehicle guidance systems have become increasingly more popular. However, despite their ubiquity, performance will always be subject to availability of detailed digital road maps. Most current digital maps are still inadequate for advanced applications in unstructured environments. Lack of up-to-date information and insufficient refinement of the road geometry are among the most important shortcomings. The massive use of inexpensive Global Positioning System (GPS) receivers, combined with the rapidly increasing availability of wireless communication infrastructure, suggests that large amounts of data combining both modalities will be available in the near future. The approach presented here draws on machine-learning techniques and processes logs of position traces to consistently build a detailed and fine-grained representation of the road network by extracting the principal paths followed by the vehicles. Although this work addresses the road-building problem in dynamic environments such as open-pit mines, it is also applicable to urban environments. New contributions include a fully unsupervised segmentation method for sampling roads and inferring the network topology, which is a general technique for extracting detailed information about road splits, merges, and intersections, as well as a robust algorithm that articulates these two. Experimental results with data from large mining operations are presented to validate the new algorithm.

Navigation and Mapping in Large Unstructured Environments

In this paper we address the problem of autonomous navigation in very large unstructured environments. A new hybrid metric map (HYMM) structure is presented that combines feature maps with other metric representations in a consistent manner. The global feature map is partitioned into a set of connected local triangular regions (LTRs), which provide a reference for a detailed multidimensional description of the environment. The HYMM framework permits the combination of efficient feature-based simultaneous localization and mapping (SLAM) algorithms for localization with, for example, occupancy grid maps for tasks such as obstacle avoidance, path planning or data association. This fusion of feature and grid maps has several complementary properties; for example, grid maps can assist data association and can facilitate the extraction and incorporation of new landmarks as they become identified from multiple vantage points. In this paper we also present a path-planning technique that efficiently maintains the estimated cost of traversing each LTR. The consistency of the SLAM algorithm is investigated with the introduction of exploration techniques to guarantee a certain measure of performance for the estimation process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithms proposed.

Approximate Inference in State-Space Models With Heavy-Tailed Noise

State-space models have been successfully applied across a wide range of problems ranging from system control to target tracking and autonomous navigation. Their ubiquity stems from their modeling flexibility, as well as the development of a battery of powerful algorithms for estimating the state variables. For multivariate models, the Gaussian noise assumption is predominant due its convenient computational properties. In some cases, anyhow, this assumption breaks down and no longer holds. We propose a novel approach to extending the applicability of this class of models to a wider range of noise distributions without losing the computational advantages of the associated algorithms. The estimation methods we develop parallel the Kalman filter and thus are readily implemented and inherit the same order of complexity. We derive all of the equations and algorithms from first principles. In order to validate the performance of our approach, we present specific instances of non-Gaussian state-space models and test their performance on experiments with synthetic and real data.

An outlier-robust Kalman filter

We introduce a novel approach for processing sequential data in the presence of outliers. The outlier-robust Kalman filter we propose is a discrete-time model for sequential data corrupted with non-Gaussian and heavy-tailed noise. We present efficient filtering and smoothing algorithms which are straightforward modifications of the standard Kalman filter Rauch-Tung-Striebel recursions and yet are much more robust to outliers and anomalous observations. Additionally, we present an algorithm for learning all of the parameters of our outlier-robust Kalman filter in a completely unsupervised manner. The potential of our approach is borne out in experiments with synthetic and real data.

Multiple target tracking using Sequential Monte Carlo Methods and statistical data association

This paper presents two approaches for the problem of multiple target tracking (MTT) and specifically people tracking. Both filters are based on sequential Monte Carlo methods (SMCM) and joint probability data association (JPDA). The filters have been implemented and tested on real data from a laser measurement system. Experiments show that both approaches are able to track multiple moving persons. A comparison of both filters is given and the advantages and disadvantages of the two approaches are presented.

Scan-SLAM: Combining EKF-SLAM and Scan Correlation

This paper presents a new generalisation of simultaneous localisation and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage of EKF-SLAM with scan correlation. Instead of geometric models, landmarks are defined by templates composed of raw sensed data, and scan correlation is shown to produce landmark observations compatible with the standard EKF-SLAM framework. The resulting Scan-SLAM combines the general applicability of scan correlation with the established advantages of an EKF implementation: recursive data fusion that produces a convergent map of landmarks and maintains an estimate of uncertainties and correlations. Experimental results are presented which validate the algorithm.

Recognising and Modelling Landmarks to Close Loops in Outdoor SLAM

In this paper, simultaneous localisation and mapping (SLAM) is combined with landmark recognition to close large loops in unstructured, outdoor environments. Camera and laser information are fused to recognise and create appearance models for landmarks. The representation is obtained through a non-linear probabilistic regression model encoding a neighbourhood preserving dimensionality reduction. A new data association algorithm is proposed where landmarks are associated based on both position and appearance. The resulting system is more robust and able to recover from possible misassociations. Experiments demonstrate the benefits of this approach in challenging problems involving mapping with large loop closings in irregular terrain, and with dynamic objects.