# Camera-Based Localization and Enhanced Normalized Mutual Information

arXiv: https://arxiv.org/abs/2412.16137 · HTML: https://mlsanity.com/paper/2412.16137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://mlsanity.com/paper/1704.05519/) (2017)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System](https://mlsanity.com/paper/1809.05477/) (2018)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [Localization Requirements for Autonomous Vehicles](https://mlsanity.com/paper/1906.01061/) (2019)
- [Developments in Modern GNSS and Its Impact on Autonomous Vehicle Architectures](https://mlsanity.com/paper/2002.00339/) (2020)

## Abstract (arXiv)

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.