Camera-Based Localization and Enhanced Normalized Mutual Information

TL;DR

This paper improves camera-based localization for autonomous vehicles by enhancing matching algorithms to better handle noise and environmental uncertainties, leading to more reliable positioning using affordable sensors.

Contribution

It introduces novel modifications to standard matching methods, particularly normalized mutual information, inspired by physical and statistical considerations, improving robustness in noisy conditions.

Findings

Enhanced NMI significantly outperforms standard methods in noisy environments.

Proposed algorithms are grounded in statistical signal processing and are provably better in some contexts.

Numerical simulations confirm the effectiveness of the modifications.

Abstract

Robust and fine localization algorithms are crucial for autonomous driving. For the production of such vehicles as a commodity, affordable sensing solutions and reliable localization algorithms must be designed. This work considers scenarios where the sensor data comes from images captured by an inexpensive camera mounted on the vehicle and where the vehicle contains a fine global map. Such localization algorithms typically involve finding the section in the global map that best matches the captured image. In harsh environments, both the global map and the captured image can be noisy. Because of physical constraints on camera placement, the image captured by the camera can be viewed as a noisy perspective transformed version of the road in the global map. Thus, an optimal algorithm should take into account the unequal noise power in various regions of the captured image, and the intrinsic uncertainty in the global map due to environmental variations. This article briefly reviews two matching methods: (i) standard inner product (SIP) and (ii) normalized mutual information (NMI). It then proposes novel and principled modifications to improve the performance of these algorithms significantly in noisy environments. These enhancements are inspired by the physical constraints associated with autonomous vehicles. They are grounded in statistical signal processing and, in some context, are provably better. Numerical simulations demonstrate the effectiveness of such modifications.