Very-large-scale distributed map making using multiple vehicles
Creators: name: Amirsadri, Ashkan
Publisher: name: The Australian National University
Year: 2015

description: The last two decades have witnessed a growing global demand for high quality, up-to-date mapping datasets from urban environments. However, the majority of current map making solutions are labour intensive, cost ineffective and error prone. This has prompted digital map publishers to seek automated solutions for creating accurate, reliable maps. This work is inspired by a real-world project called AutoMap where objects of interest and assets visible from the road scene are automatically extracted from video data captured by land vehicles and geo-located to form a map. This thesis provides a flexible solution to the problem of building and maintaining a very-large-scale map using multiple vehicles. In particular, we consider producing a map of landmarks on the scale of thousands of kilometres in an outdoor environment. A setup as described in this work enables a continuously updated database of road scene information at a fraction of the cost compared to the manual alternative. According to the proposed framework, geographically located information from the road scene is gathered continuously (over long periods of time) on a very large scale by a fleet of distributed vehicles. Each vehicle is equipped with a range of low-cost sensors including three cameras, a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), a processor and a 3G modem. In the developed solution, the mapping algorithm is distributed across different vehicles each given the task of producing and updating a local map. The vehicles selectively (and asynchronously) communicate maps to and from a central station in a bandwidth-constraint environment. This central station combines the potentially overlapping local maps to compile a global map. The developed multi-vehicle data fusion framework complies with the essential practical requirements of the AutoMap project. In particular, our solution is efficient in terms of both computational complexity and communication bandwidth. Moreover, the proposed communication architecture is scalable and is capable of dealing with time-varying overlapping map sizes. This thesis also addresses a particular, yet prominent aspect of mapping systems, namely quality assessment. A new concept of map quality for specialized road mapping applications such as AutoMap is established. We derive a particular type of error measure, known as the Directional Map Error (DIMER) metric, which is capable of reflecting the accuracy of landmark maps in a more meaningful way. The new metric can be tuned to fit a wide spectrum of mapping scenarios and can be deployed by both scientific and business communities as a tool for comparing the performance of different mapping techniques. We also devise a systematic approach known as the Covariance Trajectory Perturbation (CTP) algorithm which is capable of enhancing the DIMER-based quality of obtained maps when incorporated into an EKF-SLAM structure. The effectiveness of this approach in general single-vehicle and multi-vehicle settings are examined.