CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
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Localization is a critically essential and crucial enabler of autonomous robots. While deep learning has made significant strides in many computer vision tasks, it is still yet to make a sizeable impact on improving capabilities of metric visual localization. One of the major hindrances has been the inability of existing Convolutional Neural Network (CNN)-based pose regression methods to generalize to previously unseen places. Our recently introduced CMRNet effectively addresses this limitation by enabling map independent monocular localization in LiDAR-maps. In this paper, we now take it a step further by introducing CMRNet++ which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters. We enable this capability by moving any metric reasoning outside of the learning process. Extensive evaluations of our proposed CMRNet++ on three challenging autonomous driving datasets namely, KITTI, Argoverse, and Lyft Level5, demonstrate that our network substantially outperforms CMRNet as well as other baselines by a large margin. More importantly, for the first-time, we demonstrate the ability of a deep learning approach to accurately localize without any retraining or fine-tuning in a completely new environment and independent of the camera parameters.
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