  A potential Mars Sample Return (MSR) architecture is being jointly studied by
NASA and ESA. As currently envisioned, the MSR campaign consists of a series of
3 missions: sample cache, fetch and return to Earth. In this paper, we focus on
the fetch part of the MSR, and more specifically the problem of autonomously
detecting and localizing sample tubes deposited on the Martian surface. Towards
this end, we study two machine-vision based approaches: First, a
geometry-driven approach based on template matching that uses hard-coded
filters and a 3D shape model of the tube; and second, a data-driven approach
based on convolutional neural networks (CNNs) and learned features.
Furthermore, we present a large benchmark dataset of sample-tube images,
collected in representative outdoor environments and annotated with ground
truth segmentation masks and locations. The dataset was acquired systematically
across different terrain, illumination conditions and dust-coverage; and
benchmarking was performed to study the feasibility of each approach, their
relative strengths and weaknesses, and robustness in the presence of adverse
environmental conditions.
