Scale-invariant localization using quasi-semantic object landmarks

Andrew Holliday, Gregory Dudek

Abstract: This work presents Object Landmarks, a new type of visual feature designed for visual localization over major changes in distance and scale. An Object Landmark consists of a bounding box \({\mathbf {b}}\) defining an object, a descriptor \({\mathbf {q}}\) of that object produced by a Convolutional Neural Network, and a set of classical point features within \({\mathbf {b}}\). We evaluate Object Landmarks on visual odometry and place-recognition tasks, and compare them against several modern approaches. We find that Object Landmarks enable superior localization over major scale changes, reducing error by as much as 18% and increasing robustness to failure by as much as 80% versus the state-of-the-art. They allow localization under scale change factors up to 6, where state-of-the-art approaches break down at factors of 3 or more.

External IDs:dblp:journals/arobots/HollidayD21