Felipe Inostroza

An improved weighting strategy for Rao-Blackwellized Probability Hypothesis Density simultaneous localization and mapping

The use of random finite sets (RFSs) in simultaneous localization and mapping (SLAM) for mobile robots is a new concept that provides several advantages over traditional vector-based approaches. These include: 1) the incorporation of detection statistics, as well as the usual spatial uncertainty, in an estimation algorithm, 2) the ability to estimate the number of landmarks in a map, and 3) the circumvention of the need for data association heuristics. Solutions to SLAM can be obtained through the Rao-Blackwellized Probability Hypothesis Density (RB-PHD) filter, which is an approximation of the Bayes filter for RFSs using both particles to represent the robot trajectories, and Gaussian mixtures to represent their associated maps. This paper proposes an improved multi-feature particle weighting strategy for the RB-PHD filter and shows through simulations that it outperforms existing weighting strategies. The proposed strategy makes the RB-PHD filter a generalization of multi-hypothesis (MH) FastSLAM, a vector-based SLAM solution that uses the RB-particle filter.

Generalizing random-vector SLAM with random finite sets

The simultaneous localization and mapping (SLAM) problem in mobile robotics has traditionally been formulated using random vectors. Alternatively, random finite sets(RFSs) can be used in the formulation, which incorporates non-heursitic-based data association and detection statistics within an estimator that provides both spatial and cardinality estimates of landmarks. This paper mathematically shows that the two formulations are actually closely related, and that RFS SLAM can be viewed as a generalization of vector-based SLAM. Under a set of ideal detection conditions, the two methods are equivalent. This is validated by using simulations and real experimental data, by comparing principled realizations of the two formulations.

Metrics for Evaluating Feature-Based Mapping Performance

In robotic mapping and simultaneous localization and mapping, the ability to assess the quality of estimated maps is crucial. While concepts exist for quantifying the error in the estimated trajectory of a robot, or a subset of the estimated feature locations, the difference between all current estimated and ground-truth features is rarely considered jointly. In contrast to many current methods, this paper analyzes metrics, which automatically evaluate maps based on their joint detection and description uncertainty. In the tracking literature, the optimal subpattern assignment (OSPA) metric provided a solution to the problem of assessing target tracking algorithms and has recently been applied to the assessment of robotic maps. Despite its advantages over other metrics, the OSPA metric can saturate to a limiting value irrespective of the cardinality errors and it penalizes missed detections and false alarms in an unequal manner. This paper therefore introduces the cardinalized optimal linear assignment (COLA) metric, as a complement to the OSPA metric, for feature map evaluation. Their combination is shown to provide a robust solution for the evaluation of map estimation errors in an intuitive manner.

Multifeature-based importance weighting for the PHD SLAM filter

The probability-hypothesis-density simultaneous localization and mapping filter is a random-finite-set estimation method that incorporates the probability-hypothesis-density filter within a Rao–Blackwellized particle filter, and was developed for navigation and mapping problems. However, the filter tends to diverge due to the existing importance-weighting methods used in the Rao–Blackwellized particle filter. This article introduces a new importance-weighting method that drastically improves the robustness of the probability-hypothesis-density simultaneous localization and mapping filter. Performance evaluations are conducted using both simulations and real experimental data sets.

Relating Random Vector and Random Finite Set Estimation in Navigation, Mapping, and Tracking

Navigation, mapping, and tracking are state estimation problems relevant to a wide range of applications. These problems have traditionally been formulated using random vectors in stochastic filtering, smoothing, or optimization-based approaches. Alternatively, the problems can be formulated using random finite sets, which offer a more robust solution in poor detection conditions (i.e., low probabilities of detection, and high clutter intensity). This paper mathematically shows that the two estimation frameworks are related, and equivalences can be determined under a set of ideal detection conditions. The findings provide important insights into some of the limitations of each approach. These are validated using simulations with varying detection statistics, along with a real experimental dataset.

Genetic diversity of Bovine Viral Diarrhea Virus from cattle in Chile between 2003 and 2007

BackgroundBovine Viral Diarrhea Virus causes significant economic losses in cattle. BVDV has high genomic diversity, with two species, BVDV-1 and BVDV-2, and at least twenty-one subgenotypes for BVDV-1 and four subgenotypes for BVDV-2. Vaccines are important tools to reduce the economic losses caused by this virus. However, vaccine strains must correspond to the antigenic profile of the viruses present in the region where the vaccine is applied. A restricted phylogenetic study with 14 viruses isolated from cattle between 1993 and 2001 showed that the genetic profile of BVDV in Chile consisted of viruses of both species and sub-genotypes 1a, 1b, 1c (currently 1j) and 2a. To determine more accurately the genetic profile of BVDV in Chile, in this study a larger number of viruses obtained from bovines between 2003 and 2007 were typed.ResultsThe study was performed using partial sequences from the 5′ noncoding region (5’UTR) and E2 coding region of the viral genome of thirty-five Chilean viruses isolated from geographic regions that have 84.6% of the Chilean cattle. All tested viruses belonged to species BVDV-1. Eighteen viruses belonged to BVDV-1j subgenotype (51.4%), twelve belonged to BVDV-1b (34.3%) and five belonged to BVDV-1a (14.3%). The Chilean BVDV-1j viruses showed low genetic diversity, both among themselves and with the BVDV-1j present in other regions of the world. This could be explained by a relatively recent introduction of this viral subgenotype in cattle, which agrees with its low geographical distribution worldwide. Otherwise, Chilean BVDV-1b viruses grouped into a single cluster, different even than the viruses present in Argentina and Brazil, countries geographically close to Chile, a process of local evolution that could generate antigenic differences between the Chilean viruses and the viruses used as vaccine strains.ConclusionsThe high presence of viruses of the BVDV-1j subgenotype, which show major antigenic differences with BVDV-1a and BVDV-1b subgenotypes used in the commercial vaccines, suggest that BVDV-1j viruses could be an emergent subgenotype of BVDV in cattle in South America and suggest evaluating an update of the vaccines used in Chile.