With the proliferation of small aerial vehicles, acquiring close up imagery for high quality reconstruction is gaining importance. We present an adaptive view planning method to collect such images in an automated fashion. We first start by sampling a small set of views to build a coarse proxy to the scene. We then present (i) a method that builds a set of adaptive viewing planes for efficient view selection and (ii) an algorithm to plan a trajectory that guarantees high reconstruction quality which does not deviate too much from the optimal one. The vehicle then follows the trajectory to cover the scene, and the procedure is repeated until reconstruction quality converges or a desired level of quality is achieved. The set of viewing planes provides an effective compromise between using the entire 3D free space and using a single view hemisphere to select the views. We compare our algorithm to existing methods in three challenging scenes. Our algorithm generates views which produce the least reconstruction error comparing to three different baseline approaches.
|Original language||English (US)|
|Title of host publication||2019 International Conference on Robotics and Automation, ICRA 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - May 2019|
|Event||2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada|
Duration: May 20 2019 → May 24 2019
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2019 International Conference on Robotics and Automation, ICRA 2019|
|Period||5/20/19 → 5/24/19|
Bibliographical noteFunding Information:
*This work was supported by NSF Awards 1525045 and a Minnesota State LCCMR grant Cheng Peng and Volkan Isler are with the Department of Computer Science, University of Minnesota, Twin Cities, Minneapolis, MN, 55455
© 2019 IEEE.