Enhancing Localization Accuracy with Sensor Fusion Techniques in Unknown Environments

Authors

Priyanka Das

Abstract

The purpose of this paper is to discuss advanced approaches to sensor fusion, such as EKF SLAM, Graph-based SLAM, and virtual-inertial SLAM (VISLAM), with methods to improve the quality of a sensor such as Loop Closure Detection and Dynamic Weighting. It also incorporates necessary measures to enhance the performance of SLAM. The paper established that navigation in an unknown environment is a critical feature for autonomous systems, and it relies on accurate localization and mapping with no prior information. The report further affirmed that multi-sensor fusion methods coordinate information acquired from different sensors like LiDAR, cameras, and IMU to improve localization quality and avoid dependence on a solitary sensor. These findings contribute to the development of more realistic and scalable SLAM systems to model and navigate through challenging and dynamic environments. As technologies continue to evolve, there is a possibility for the emergence of advanced systems in robotics, self-driving cars, and similar technologies further to improve the performance of SLAM systems in unknown environments.

Keywords

SLAM, Sensor, Performance, Unknown Environment, Localization

Enhancing Localization Accuracy with Sensor Fusion Techniques in Unknown Environments. Priyanka Das. 2022. IJIRCT, Volume 8, Issue 1. Pages 1-5. https://www.ijirct.org/viewPaper.php?paperId=2411100