  We present a protocol to construct your own depth validation dataset for
navigation. This protocol, called RDC for Rigid Depth Constructor, aims at
being more accessible and cheaper than already existing techniques, requiring
only a camera and a Lidar sensor to get started. We also develop a test suite
to get insightful information from the evaluated algorithm. Finally, we take
the example of UAV videos, on which we test two depth algorithms that were
initially tested on KITTI and show that the drone context is dramatically
different from in-car videos. This shows that a single context benchmark should
not be considered reliable, and when developing a depth estimation algorithm,
one should benchmark it on a dataset that best fits one's particular needs,
which often means creating a brand new one. Along with this paper we provide
the tool with an open source implementation and plan to make it as
user-friendly as possible, to make depth dataset creation possible even for
small teams. Our key contributions are the following: We propose a complete,
open-source and almost fully automatic software application for creating
validation datasets with densely annotated depth, adaptable to a wide variety
of image, video and range data. It includes selection tools to adapt the
dataset to specific validation needs, and conversion tools to other dataset
formats. Using this application, we propose two new real datasets, outdoor and
indoor, readily usable in UAV navigation context. Finally as examples, we show
an evaluation of two depth prediction algorithms, using a collection of
comprehensive (e.g. distribution based) metrics.
