, Simplifying the scale factor determination by normalizing DepthNet

. .. Depthnet, 113 6.5 Our proof of concept

, Conclusions for this chapter

, Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network, This chapter aims at studying the possibilities of using our network DepthNet to get real-time depth maps, 2017.

.. .. , Perspectives and Conclusions Contents 7.1 Limitations and future

, 119 7.2.2 Robust and evolutive passive depth sensing, p.119

, We showed in this work a working strategy to train a neural network to sense depth based on motion from a stabilized camera, for any kind of domain. We even showed that the next step in the general obstacle avoidance strategy, which was to train a neural network to avoid obstacles from a perfect depth map provided by a simulator before concatenating it with a real depth sensing solution might be replaced by a differentiable MPC

, The strategy presented in the introduction (section 1.4.3) and in figure 1.8 is still valid and might be the subject for future work. However, thanks to a thorough study on possible drawbacks of depth from vision algorithms, we could identify several drawbacks of DepthNet that might need to be solved

, On the other hand, due to the lack of other validation sets for depth sensing in the context of outdoor flight, we could only have a subjective validation for the UAV use-case. It thus seems necessary to construct a validation set with reliable ground-truth depth and navigation data the same way KITTI was constructed. Several solutions can be considered: ? Construct a UAV with a calibrated Lidar, similarly to KITTI

. Mre({-?,

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