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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    $${\mathbf {b}}$$  b defining an object, a descriptor    $${\mathbf {q}}$$  q of that object produced by a Convolutional Neural Network, and a set of classical point features within    $${\mathbf {b}}$$  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.

Scale-invariant localization using quasi-semantic object landmarks