Multi-sensors Error Modeling and Integration for Future Autonomous Car Navigation

Nowadays, there is a rapid increase in the use of low-cost inertial navigation sensors for commercial and civil applications. Fully autonomous or remotely controlled vehicles requires a reliable and continuous navigation system providing meter level accuracy. The cost, size, and power demand of navigation systems providing this level of accuracy impose a limiting factor to numerous applications. To provide a viable and alternative option, this research will focus on developing error models for multiple dead reckoning low-cost sensors, using innovative approaches, to mitigate the limitations on the performance of Global Navigation Satellite Systems (GNSS) for navigation in urban environments. Furthermore, the research entails developing a set of methods and algorithms for both data acquisition and processing, respectively. The main outcome of the proposed research will be a robust navigation system that entails hardware and software that is capable of providing accurate navigation solutions for autonomous and self-driving car navigation

Faculty Supervisor:

Steve Liang

Student:

Partner:

Profound Positioning Inc

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Calgary

Program: