- Doctoral Thesis
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Visual (self)localization enables Autonomous Ground Vehicles (AGVs) to assess their position and orientation within an environment with up to centimeter level accuracy, using only cost-effective camera sensors. Especially for high precision maneuvering in GNSS-denied environments, using cameras for localization may be the best suited option for budget- or weight constrained platforms. However, particularly in outdoor environments, camera images are subject to various forms of appearance change. This renders it challenging to reliably localize a vehicle against a map previously built from sensor data recorded under different appearance conditions. A powerful approach to deal with these appearance changes is to enhance the map with visual data from several recordings, each collected under different appearance conditions. The amount of data generated following this approach, however, scales with the number of recordings collected over time, and thus unveils a need for smart algorithms managing this data and ensuring efficient use of computation, storage and network bandwidth resources. The contributions of this thesis are centered around the research questions addressing this need for a resource-efficient and reliable visual localization system for AGVs in outdoor environments. In Part A, we propose an algorithm to dynamically select small amounts of map data matching the current appearance condition, thereby lowering network bandwidth consumption, and reducing computational demands on the vehicle platforms. We show that exploiting co-observability statistics allows for performing this appearance-based map data selection in a highly effective manner, without the need to explicitly model or enumerate the different appearance conditions. Part B is devoted to the development of a practical map management process for a visual localization system targeted at long-term use. Our experiments have revealed that multi-session maps converge to a relatively stable state after several months of collecting recordings under varying appearance conditions. Furthermore, through a tight integration of appearance-based map data selection with offline map summerization, a completely scalable visual localization and mapping framework is reached that can be used for indefinite periods of time. In Part C, we present the visual localization system developed within the UP-Drive project 1 for autonomous cars in urban outdoor environments. Thereby, a special focus has been placed on robustness against outdoor and long-term ap- pearance change, and on a careful evaluation of the localization accuracy. We demonstrate that reliable and accurate visual localization is feasible in structured outdoor environments, even over long time spans, across vastly different seasonal, weather, and lighting conditions including at night-time, and with local point features with binary descriptors on a CPU-only computer architecture. Show more
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ContributorsExaminer: Siegwart, Roland
Examiner: Tardós, Juan
Organisational unit03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
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