Monocular Vision based Particle Filter Localization in Urban Environments

This thesis presents the design and experimental result of a monocular vision based particle filter localization system for urban settings that uses aerial orthoimagery as a reference map. The topics of perception and localization are reviewed along with their modeling using a probabilistic framework. Computer vision techniques used to create the feature map and to extract features from camera images are discussed. Localization results indicate that the design is viable

Development of a Microscopic Traffic Simulator for Inter-Vehicle Communication Application Research

This paper describes the development of a microscopic traffic simulator purposely designed for ITS researchers studying inter-vehicle communication (IVC) concepts and applications in large traffic networks. The simulator can represent real life vehicles within the simulation by using data from vehicle Global Positioning System (GPS) receivers, enabling validation of theories with real vehicle data. The software is developed on top of the existing microscopic traffic simulator VISSIM with the added flexibility of modelling and efficiently handling communication between large numbers of vehicles. This along with the software architecture was discussed in detail

Cooperative Localization and Mapping in Sparsely-communicating Robot Networks

This thesis examines the use of multiple robots in cooperative simultaneous localization and mapping (SLAM), where each robot must estimate the poses of all robots in the team, along with the positions of all known landmarks. The robot team must operate under the condition that the communication network between robots is never guaranteed to be fully connected. Under this condition, a novel algorithm is derived that allows each robot to obtain the centralized-equivalent estimate in a decentralized manner, whenever possible. The algorithm is then extended to a decentralized and distributed approach where robots share the computational burden in considering different data association hypotheses in generating the centralized-equivalent consensus estimate.Ph

Localization in Urban Environments by Matching Ground Level Video Images with an Aerial Image

This paper presents the design of a monocular vision based particle filter localization system for urban settings that uses aerial orthoimagery as the reference map. One of the design objectives is to provide a low cost method for outdoor localization using a single camera. This relaxes the need for global positioning system (GPS) which may experience degraded reliability in urban settings. The second objective is to study the achievable localization performance with the aforementioned resources. Image processing techniques are employed to create a feature map from an aerial image, and also to extract features from camera images to provide observations that are used by a particle filter for localization

Markov-Based Lane Positioning Using Intervehicle Communication

The majority of today\u27s navigation techniques for intelligent transportation systems use global positioning systems (GPS) that can provide position information with bounded errors. However, due to the low accuracy that is experienced with standard GPS, it is difficult to determine a vehicle\u27s position at lane level. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within communication range, the lane positions of vehicles can be found. The algorithm\u27s effectiveness is shown in both simulations and experiments with real data

Co-operative Lane-Level Positioning Using Markov Localization

The majority of today\u27s navigation techniques for intelligent transportation systems use Global Positioning Systems (GPS) that can provide position information with bounded errors. However, because of the low accuracy and multi-path problem, it is challenging to determine a vehicle\u27s position at lane level. With Markov-based approach based on sharing information among a group of vehicles that are traveling close to each other, the lane positions of vehicles can be found. The algorithm shows its effectiveness in both simulations and experiments with real data