On the use of unmanned aerial vehicles for autonomous object modeling

In this paper we present an end to end object modeling pipeline for an unmanned aerial vehicle (UAV). We contribute a UAV system which is able to autonomously plan a path, navigate, acquire views of an object in the environment from which a model is built. The UAV does collision checking of the path and navigates only to those areas deemed safe. The data acquired is sent to a registration system which segments out the object of interest and fuses the data. We also show a qualitative comparison of our results with previous work.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Youfu Li,et al.  Information entropy-based viewpoint planning for 3-D object reconstruction , 2005, IEEE Transactions on Robotics.

[3]  Franz S. Hover,et al.  Three-dimensional coverage planning for an underwater inspection robot , 2013, Int. J. Robotics Res..

[4]  Markus Vincze,et al.  Autonomous Learning of Object Models on a Mobile Robot , 2017, IEEE Robotics and Automation Letters.

[5]  Gerd Hirzinger,et al.  View Planning for Multi-View Stereo 3D Reconstruction Using an Autonomous Multicopter , 2012, J. Intell. Robotic Syst..

[6]  Lucas Beyer,et al.  The STRANDS Project: Long-Term Autonomy in Everyday Environments , 2016, IEEE Robotics Autom. Mag..

[7]  Wei Jing,et al.  View planning for 3D shape reconstruction of buildings with unmanned aerial vehicles , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[8]  Ben M. Chen,et al.  A robust online path planning approach in cluttered environments for micro rotorcraft drones , 2016 .

[9]  Rares Ambrus,et al.  Meta-rooms: Building and maintaining long term spatial models in a dynamic world , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Jyun-Min Dai,et al.  Path planning and obstacle avoidance for vision guided quadrotor UAV navigation , 2016, 2016 12th IEEE International Conference on Control and Automation (ICCA).

[11]  Dieter Fox,et al.  Toward object discovery and modeling via 3-D scene comparison , 2011, 2011 IEEE International Conference on Robotics and Automation.

[12]  John J. Leonard,et al.  Toward lifelong object segmentation from change detection in dense RGB-D maps , 2013, 2013 European Conference on Mobile Robots.

[13]  Yoav Y. Schechner,et al.  The Next Best Underwater View , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Markus Vincze,et al.  RGB-D object modelling for object recognition and tracking , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[15]  G. Roth,et al.  View planning for automated three-dimensional object reconstruction and inspection , 2003, CSUR.

[16]  Changchang Wu,et al.  SiftGPU : A GPU Implementation of Scale Invariant Feature Transform (SIFT) , 2007 .