Benjamin Adler
University of Hamburg
6 Papers
18 Citations
Benjamin Adler is an academic researcher from University of Hamburg. The author has contributed to research in topics: Point cloud & Software architecture. The author has an hindex of 5, co-authored 6 publications.
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Papers
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
TL;DR: These algorithms have been evaluated using real-world datasets from both structured and unstructured environments and benchmarked against a state-of-the-art point-based region growing (PBRG) algorithm with regard to segmentation speed.
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Planar Segment Based Three-dimensional Point Cloud Registration in Outdoor Environments
TL;DR: Experimental results confirm that the approach offers an alternative to state‐of‐the‐art algorithms in plane‐rich environments and contains robustness with respect to occlusions and partial observations, and registration accuracy compared to ground truth.
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Towards autonomous airborne mapping of urban environments
Benjamin Adler,Junhao Xiao +1 more
- 12 Nov 2012
TL;DR: This work documents the progress on building an unmanned aerial vehicle capable of autonomously mapping urban environments, and presents the algorithm's application to real sensor-data and analyze its performance in a virtual outdoor scenario.
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User-driven software design for an elderly care service robot
Norman Hendrich,Hannes Bistry,Benjamin Adler,Jianwei Zhang +3 more
- 20 May 2014
TL;DR: This paper describes a service- and scenario-driven software architecture for the ambient assisted living infrastructure currently under development in the Robot-Era project, with a focus on the integration of the ROS-based sensing and manipulation capabilities with precise indoor navigation and the PEIS middleware for ubiquitous robotics.
7
Finding next best views for autonomous UAV mapping through GPU-accelerated particle simulation
Benjamin Adler,Junhao Xiao,Jianwei Zhang +2 more
- 01 Nov 2013
TL;DR: A novel algorithm capable of generating multiple next best views (NBVs), sorted by achievable information gain, designed for way-point generation in autonomous airborne mapping of outdoor environments, and can be used with any sensor generating spatial occupancy information.
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