Mapping Indoor Environments Based on Human Actvity |
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| We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types. |
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Related documents: PhdThesis: Mapping, State Estimation, and Navigation for Quadrotors and Human-Worn Sensor Systems TRO Journal paper (2012): Activity-based Estimation of Human Trajectories ICRA 10: Mapping Indoor Environments Based on Human Activity see also www.slawomir.de |
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Videosgreen: doorsgray: trajectory red: current pose blue circles: uncertainty of the doors w.r.t. the current pose All videos display the same information in two ways. On the left You see the full trajectory whereas on the right only the current trajectory is shown. In both cases only incremental updates are performed. The full trajectory is shown for better comparison only. For better visibility, the uncertainty circles are not drawn in the movie shown right. |
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Experiment #1: length of trajectory about 1.6km If the video does not play, download it here 
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Experiment #2: length of trajectory about 1.3km If the video does not play, download it here 
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Experiment #3: If the video does not play, download it here 
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Extension towards 3D reconstructionRecently, we successfully extended our approach towards 3D reconstruction by including the activity of walking up/down a staircase. The next image shows one of our experiments. It contains a trajectory of approximately 2.2km covering 3 floors.The first image displays the raw information given the raw data from the Data Suit and the raw data from detecting doors and stairs. The second image shows the result of our 3D reconstruction approach. The remaining images show an overlay of the trajectory over the floorplan of the same building. Videos of different runs including 3D reconstruction can be found below. |
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Experiment #4: length of trajectory about 2.2km If the video does not play, download it here 
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Experiment #5: length of trajectory about 2.85km If the video does not play, download it here 
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Experiment #6: length of trajectory about 1.46km If the video does not play, download it here 
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