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.

MapAware SLAM with Sparse Map Features Localization is a key capability for autonomous vehicles. HighDefinition maps are a popular method to represent the environment and to enable precise localization. However, the creation is very demanding and it is not always guaranteed to receive accurate map information, especially for unstructured areas. In this paper, we introduce a novel probabilistic localization and mapping framework that brings together the advantages of sparse feature maps, multitarget tracking for landmark detection, probabilistic global vehicle localization and a graphbased formulation to achieve a consistent map. The frontend of our Simultaneous Localization and Mapping framework is based on Monte Carlo Localization. Our novel measurement model integrates a virtual topological PathMap with sparse map features to obtain global localization. The graphbased backend optimizes online the vehicle trajectory and the landmarksxe2x80x99 configuration to create a globally aligned map. Furthermore, our method allows weaker requirements in terms of accuracy of the sparse feature map as we represent the degree of uncertainty by means of probabilistic distribution. Additionally, the sparse feature map representation needs substantially less memory than other approaches, which is an advantage for autonomous vehicles. The framework has been tested and evaluated in real experiments for several autonomous runs. The results demonstrate the robustness of our system.