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Building the Future of Transportation: A Comprehensive Survey on AV Perception, Localization, and Mapping.

Ashok Kumar Patil¹, Bhargav Punugupati¹, Himanshi Gupta¹

¹Department of Computer Science Engineering, PES University, Bangalore 560085, India.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This review covers perception, localization, and mapping for autonomous vehicles (AVs). It analyzes object detection, tracking, and mapping techniques, offering insights for AV development.

Keywords:

HD maps LiDAR SLAM autonomous vehicles localization mapping object detection perception module sensor fusion tracking algorithms

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision and Sensor Fusion

Background:

Autonomous vehicles (AVs) require sophisticated perception, localization, and mapping systems for safe navigation.
Existing methodologies face challenges in complex urban environments and GPS-denied scenarios.

Purpose of the Study:

To review and analyze current methodologies and best practices in AV perception, localization, and mapping.
To provide insights into the effectiveness of various techniques through real-world testing.

Main Methods:

Object detection (e.g., YOLOv8) and tracking (e.g., ByteTrack, BoT-SORT).
Localization techniques including LiDAR-based, camera-based, and sensor fusion.
Mapping strategies such as SLAM and HD maps.

Main Results:

Evaluation of real-time performance and robustness of perception algorithms.
Assessment of localization accuracy in GPS-unreliable conditions.
Analysis of mapping techniques for detailed environmental representation.

Conclusions:

Summarizes advancements, challenges, and practical considerations in AV perception, localization, and mapping.
Serves as a reference for researchers and developers in the autonomous vehicle domain.