Abstract-A lot of research has been done in the area of Simultaneous Localization and Mapping (SLAM). It is particularly challenging in underwater environments where many of the self-localization methods used at land no longer work. This paper presents a method to use a rotating scanning sonar as the sole sensor to perform SLAM in harbors or natural waterways. We present a feature extraction process which is capable of extracting walls of arbitrary shape. These extracted wall features are then used to perform SLAM using the wellknown FastSLAM algorithm. We show that SLAM is possible given this type of sensor and using our feature extraction process. The algorithm was validated on an open water test site and will be shown to provide localization accuracy generally within the error of the GPS ground truth.

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In this paper we present a real-time graph-based visual SLAM approach. The presented visual SLAM algorithm can be separated into three parts: feature extraction, data association, and SLAM back-end. We use FAST for feature detection and the Binary Robust Independent Elementary Features (BRIEF) as feature descriptor, which together provide a fast and stable feature extraction. The data association is solved using Locality Sensitive Hashing (LSH), which uses local hash tables and profits from binary feature descriptors. As SLAM back-end we use the general graph optimization framework g 2 o, which is designed to provide solutions to several SLAM variants. We further provide a novel approach to visual odometry by combining recent sensor measurements into a small pose graph and optimizing it using g 2 o. For finding potential neighbour nodes and loop closures we introduce the Global Feature Repository (GFR). GFR searches for loop closures and potential neighbours independent of their position in the graph. Finally, we show the accuracy and real-time ability of our algorithm by comparing it to a recently published benchmark dataset. We further provide some large-scale datasets using state-of-the-art laser localization as ground-truth.

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