Abstract-This paper presents the first algorithm for simultaneous localization and mapping (SLAM) that can estimate the locations of both dynamic and static features in addition to the vehicle trajectory. We model the feature-based SLAM problem as a singlecluster process, where the vehicle motion defines the parent, and the map features define the daughter. Based on this assumption, we obtain tractable formulae that define a Bayesian filter recursion. The novelty in this filter is that it provides a robust multi-object likelihood which is easily understood in the context of our starting assumptions. We present a particle/Gaussian mixture implementation of the filter, taking into consideration the challenges that SLAM presents over target tracking with stationary sensors, such as changing fields of view and a mixture of static and dynamic map features. Monte Carlo simulation results are given which demonstrate the filter's effectiveness with high measurement clutter and non-linear vehicle motion.

Object triangulation, 3-D object tracking, feature correspondence, and camera calibration are key problems for estimation from camera networks. This paper addresses these problems within a unified Bayesian framework for joint multiobject tracking and sensor registration. Given that using standard filtering approaches for state estimation from cameras is problematic, an alternative parametrisation is exploited, called disparity space. The disparity space-based approach for triangulation and object tracking is shown to be more effective than non-linear versions of the Kalman filter and particle filtering for non-rectified cameras. The approach for feature correspondence is based on the Probability Hypothesis Density (PHD) filter, and hence inherits the ability to update without explicit measurement association, to initiate new targets, and to discriminate between target and clutter. The PHD filtering approach then forms the basis of a camera calibration method from static or moving objects. Results are shown on simulated data.

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Linked foraging and bioenergetics models allow for increased understanding of fish growth potential and behavior by incorporating prey availability coupled to environmental conditions including temperature and prey visibility. To inform our understanding of growth and vertical migration patterns of Chinook salmon (Oncorhynchus tshawytscha) inhabiting lentic ecosystems, we linked foraging and bioenergetics models to create GrowChinook ( http://growchinook.fw.oregonstate.edu/ ). This multimodel design and optimization routine has broad applications in examining growth potential and predicting habitat use in stratified environments. We demonstrate the use of GrowChinook for the spring–summer rearing period in three Willamette River basin reservoirs, Oregon, USA. These reservoirs support juvenile spring Chinook salmon that exhibit a novel reservoir-reared life history that includes larger juvenile fish compared with nearby stream-reared subyearlings. Model outputs of predicted growth and depth use patterns based on observed prey distributions and environmental conditions were corroborated by observed empirical size and growth data from other years. Our simulations support diel vertical migration as a tactic that increases growth potential and contribute to understanding juvenile Chinook salmon growth in stratified systems.