Robots that use vision for localization need to handle environments that are subject to seasonal and structural change, and operate under changing lighting and weather conditions. We present a framework for lifelong localization and mapping designed to provide robust and metrically accurate online localization in these kinds of changing environments. Our system iterates between offline map building, map summary, and online localization. The offline mapping fuses data from multiple visually varied datasets, thus dealing with changing environments by incorporating new information. Before passing these data to the online localization system, the map is summarized, selecting only the landmarks that are deemed useful for localization. This Summary Map enables online localization that is accurate and robust to the variation of visual information in natural environments while still being computationally efficient. We present a number of summary policies for selecting useful features for localization from the multisession map, and we explore the tradeoff between localization performance and computational complexity. The system is evaluated on 77 recordings, with a total length of 30 kilometers, collected outdoors over 16 months. These datasets cover all seasons, various times of day, and changing weather such as sunshine, rain, fog, and snow. We show that it is possible to build consistent maps that span data collected over an entire year, and cover day‐to‐night transitions. Simple statistics computed on landmark observations are enough to produce a Summary Map that enables robust and accurate localization over a wide range of seasonal, lighting, and weather conditions.
We present the local path planning and obstacle avoidance method used on the autonomous tour-guide robot RoboX. It has proven its value during a 5 month operation of ten such robots in a real-world application, a very crowded exhibition. Three known approaches (DWA, elastic band, NF1) have been integrated into a system that performs smooth motion efficiently, in the sense of computational effort as well as goal-directedness. Apart from modifications to the DWA and the elastic band, we present the formulations that allow this fusion.
Abstract-The E* algorithm is a path planning method capable of dynamic replanning and user-configurable path cost interpolation. It calculates a navigation function as a sampling of an underlying smooth goal distance that takes into account a continuous notion of risk that can be controlled in a fine-grained manner. E * results in more appropriate paths during gradient descent. Dynamic replanning means that changes in the environment model can be repaired to avoid the expenses of complete replanning. This helps compensating for the increased computational effort required for interpolation.We present the theoretical basis and a working implementation, as well as measurements of the algorithm's precision, topological correctness, and computational effort.Index Terms-Mobile robot path planning, dynamic replanning
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