Long-Distance Loop Closure Using General Object Landmarks
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Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitudes, or performing loop closure over long distances. Existing approaches, however, are robust only up to a 3x difference in scale between map and query images. We propose a novel combination of deep-learning-based object features and hand-engineered point-features that yields improved robustness to scale change, perspective change, and image noise. We conduct experiments in simulation and in real-world outdoor scenes exhibiting up to a 7x change in scale, and compare our approach against localization using state-of-the-art SIFT features. This technique is training-free and class-agnostic, and in principle can be deployed in any environment out-of-the-box.
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