Long-Term Simultaneous Localization and Mapping in Dynamic Environments

One of the core competencies required for autonomous mobile robotics is the ability to use sensors to perceive the environment. From this noisy sensor data, the robot must build a representation of the environment and localize itself within this representation. This process, known as simultaneous localization and mapping (SLAM), is a prerequisite for higher-level autonomous behavior in many robotic applications. SLAM has been well studied under the assumptions that the robot operates for a relatively short time period and that the environment is essentially static during operation. However, performing SLAM over long time periods while modeling the dynamic changes in the environment remains a challenge. The goal of this thesis is to extend the capabilities of SLAM to enable long-term autonomous operation in dynamic environments. The contribution of this thesis has three main components: First, we propose a framework for controlling the computational complexity of the SLAM optimization problem so that it does not grow unbounded with exploration time. Second, we present a method to learn visual feature descriptors that are more robust to changes in lighting, allowing for improved data association in dynamic environments. Finally, we use the proposed sparse-approximate marginalization and learned visual features in a SLAM system that explicitly models the dynamics of the environment in the map by representing each location as a set of example views that capture how the location changes with time.