Vision-Aided Navigation: Improved Measurements Models and a Data Driven Approach

Vision-aided navigation is the process of fusing data from visual cameras with other information sources to provide vehicle state estimation. Fusing information from multiple sources in a statistically optimal manner requires accurate stochastic models of each information source. Developing such a model for visual measurements presents a number of challenges. Vision-aided navigation systems rely on a set of computer vision methods known as feature detection and tracking to abstract visual camera images into a data source amenable to state estimation. It is nearly universally assumed that the measurements produced by these methods have independent and identically distributed (IID) errors. This study presents evidence that directly contradicts these assumptions. Novel models for visual measurements that eliminate the IID assumption are developed. Estimators are designed around the models and tested. Results demonstrate a significant performance advantage over existing methods and also reveal new challenges and paradoxes that motivate further research. In addition to improving vision-aided navigation models, a set of flexible and robust data-driven estimation techniques are developed and demonstrated on both canonical problems and problems in vision-aided navigation

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp