learning for active 3d mapping - cmpcmp.felk.cvut.cz/~zimmerk/iccv_2017/iccv17_poster.pdf ·...
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International Conference on Computer Vision 2017
Learning for Active 3D Mapping
Karel Zimmermann, Tomáš Petříček, Vojtěch Šalanský, Tomáš Svoboda Czech Technical University in Prague
Problem definition Motivation: New Solid State Lidars will allow independent steering of depth measuring rays.
Goal 1. Learn to reconstruct dense 3D voxel map from sparse depth
measurements 2. Optimize reactive control of depth-measuring rays along an
expected vehicle trajectory
Overview of active 3D mapping pipeline
➢ Learning of and planning of optimize common objective:
Learning of 3D mapping network ➢ Learning searches for parameters ➢ Result of planning is not differentiable wrt ➢ Objective is first locally approximated around an initial point
and then optimized by SGD
➢ The optimization is iterated
➢ Fix point of this mapping would assure: 1. local optimality of the objective 2. statistical consistency of the learning process ➢ In practise, we iterate until validation error stops decreasing
Planning of depth measuring rays ➢No ground truth available online ➢Objective approximated from currently available map
➢Planning of rays for following L positions si approximated as
Algorithm properties ➢ 500x less reestimations ➢ 50x faster then naive ➢ 50ms for planning
(1e6 voxels, 1e5 rays) ➢Derived upper bound on
the approximation ratio
Experiments Structure of 3D mapping net Qualitative evaluation
Quantitative evaluation ➢Training: 20 seq. from “Kitty Residential category” ➢Testing: 13 seq. from “Kitty: City category” ➢Local maps 320x320x32 voxels
(1voxel~20cm 64m x 64m x 6.4m) ➢Selected K=200 rays per position out of 19200 ➢Horizont of L=5 positions
Acknowledgement: The research leading to these results has received funding from the European Union under grant agreement FP7-ICT-609763 TRADR and No.692455 Enable-S3, from the Czech Science Foundation under Project 17-08842S, and from the Grant Agency of the CTU in Prague under Project SGS16/161/OHK3/2T/13. Images of S3 Lidar redistributed with permission of Quanergy Systems (http://quanergy.com)
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[1] Choy et al., A unified approach for single a multi-view3D object reconstruction, ECCV, 2016
Full setting
See video online:
Emitted laser beams are splitted
Transimtted through Optical Phased Array
Controlling optical properties of OPA elements, allows to steer laser beams into desire directionReflected laser beams captured by SPAD array
Prioritized Greedy algorithm
UB
⇣proposed
optimal
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optimal coverage
30%
planning
learning
⇥
current loss in voxel iprob. that voxel iis not visible by any ray j 2 J
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Limited setting