FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

Gokhale, Vineet; Barrera, Gerardo Moyers; Prasad, R. Venkatesha

doi:10.1109/icc42927.2021.9500500

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…In this context, localization takes center stage. This paper [204] presents FEEL-an inventive indoor localization system that amalgamates three low-energy sensors: IMU (Inertial Measurement Unit), UWB (Ultra-Wideband), and radar. The authors elaborate on FEEL's software and hardware architecture.…”

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confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

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The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

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confidence: 99%