Localization in Autonomous Vehicles Using a Generalized Inner Product Fine localization in autonomous driving platforms is a task of broad interest, receiving much attention in recent years. Some localization algorithms use the Euclidean distance as a similarity measure between the local image acquired by a camera and a global map, which acts as side information. The global map is typically expressed in terms of the coordinate system of the road plane. Yet, a road image captured by a camera is subject to distortion in that nearby features on the road have much larger footprints on the focal plane of the camera compared with those of equallysized features that lie farther ahead of the vehicle. Using commodity computational tools, it is straightforward to execute a transformation and, thereby, bring the distorted image into the frame of reference of the global map. However, this nonlinear transformation results in unequal noise amplification. The noise profile induced by this transformation should be accounted for when trying to match an acquired image to a global map, with more reliable regions being given more weight in the process. This physical reality presents an algorithmic opportunity to improve existing localization algorithms, especially in harsh conditions. This article reviews the physics of road feature acquisition through a camera, and it proposes an improved matching method rooted in statistical analysis. Findings are supported by numerical simulations.

Degeneracy in SelfCalibration Revisited and a Deep Learning Solution for Uncalibrated SLAM Selfcalibration of camera intrinsics and radial distortion has a long history of research in the computer vision community. However, it remains rare to see real applications of such techniques to modern Simultaneous Localization And Mapping (SLAM) systems, especially in driving scenarios. In this paper, we revisit the geometric approach to this problem, and provide a theoretical proof that explicitly shows the ambiguity between radial distortion and scene depth when twoview geometry is used to selfcalibrate the radial distortion. In view of such geometric degeneracy, we propose a learning approach that trains a convolutional neural network (CNN) on a large amount of synthetic data. We demonstrate the utility of our proposed method by applying it as a checkerboardfree calibration tool for SLAM, achieving comparable or superior performance to previous learning and handcrafted methods.