Robust Visual Localization in Compute-Constrained Environments by Salient Edge Rendering and Weighted Hamming Similarity
This enables reliable object localization for robotic arms in space missions and other compute-limited settings, representing a domain-specific advancement.
The paper tackles vision-based 6-DoF object pose estimation for robotic operations in compute-constrained environments like Mars missions, proposing a method that uses a custom renderer and template matching metric to achieve robust localization with low-fidelity 3D models. Results show it consistently beats state-of-the-art methods in robustness and accuracy under hardware constraints.
We consider the problem of vision-based 6-DoF object pose estimation in the context of the notional Mars Sample Return campaign, in which a robotic arm would need to localize multiple objects of interest for low-clearance pickup and insertion, under severely constrained hardware. We propose a novel localization algorithm leveraging a custom renderer together with a new template matching metric tailored to the edge domain to achieve robust pose estimation using only low-fidelity, textureless 3D models as inputs. Extensive evaluations on synthetic datasets as well as from physical testbeds on Earth and in situ Mars imagery shows that our method consistently beats the state of the art in compute and memory-constrained localization, both in terms of robustness and accuracy, in turn enabling new possibilities for cheap and reliable localization on general-purpose hardware.