Sebastian Hilsenbeck

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We propose a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian indoor positioning using mobile devices. We employ our fusion technique to combine relative motion information based on step detection with WiFi signal strength measurements. The method is based on the well-known particle filter methodology. In contrast to previous(More)
Recent advances in the field of content-based image retrieval (CBIR) have made it possible to quickly search large image databases using photographs or video sequences as a query. With appropriately tagged images of places, this technique can be applied to the problem of visual location recognition. While this task has attracted large interest in the(More)
—We present a visual odometry system for indoor navigation with a focus on long-term robustness and consistency. As our work is targeting mobile phones, we employ monocular SLAM to jointly estimate a local map and the device's trajectory. We specifically address the problem of estimating the scale factor of both, the map and the trajectory. State-of-the-art(More)
—Distinctive visual cues are of central importance for image retrieval applications, in particular, in the context of visual location recognition. While in indoor environments typically only few distinctive features can be found, outdoors dynamic objects and clutter significantly impair the retrieval performance. We present an approach which exploits text,(More)
State-of-the-art visual odometry algorithms achieve remarkable efficiency and accuracy. Under realistic conditions, however, tracking failures are inevitable and to continue tracking, a recovery strategy is required. In this paper, we propose a relocalization system that enables realtime, 6D pose recovery for wide baselines. Our approach targets(More)
Determining the pose of a mobile device based on visual information is a promising approach to solve the indoor localization problem. We present an approach that transforms localized images along a mapping trajectory into virtual viewpoints that cover a set of densely sampled camera positions and orientations in a confined environment. The viewpoints are(More)
Recent progress in the field of content-based image retrieval has enabled camera-based indoor positioning. The matching of smart-phone recordings with a database of geo-referenced images allows for meter accurate infrastructure-free localization. In mobile scenarios , however, three major constraints have to be considered: limited computational resources of(More)
We present a novel user interface concept for indoor navigation which uses directional arrows and panorama images of decision points, such as turns, along the route. The interface supports the mental model of landmark-based navigation, can be used on-and offline, and is highly tolerant to localization inaccuracy. We evaluated the system in a real-world user(More)
In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output in spatial coordinates in order to capture contextual information.(More)