Optimized State Estimation for Real-Time Robotic Navigation and Mapping

This study explores an advanced state estimation framework for robotic localization and mapping using a probabilistic filtering approach. By leveraging an Extended Kalman Filter (EKF), the system continuously refines position estimates through sensor

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Publication Details

Published
2025-08-19
DOI
https://doi.org/10.36227/techrxiv.175562858.83589710/v1
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Optimized State Estimation for Real-Time Robotic Navigation and Mapping

Rishabh Satish Changwani
Robotic Path Planning Algorithms
article

Optimized State Estimation for Real-Time Robotic Navigation and Mapping

Rishabh Satish Changwani
article en

Abstract

This study explores an advanced state estimation framework for robotic localization and mapping using a probabilistic filtering approach. By leveraging an Extended Kalman Filter (EKF), the system continuously refines position estimates through sensor

University at Buffalo, State University of New York (US)
Openalex Percentile: Top 80%
Robotic Path Planning Algorithms
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Optimized State Estimation for Real-Time Robotic Navigation and Mapping — Rishabh Satish Changwani · (2025) | TGRS Research Map | TGRS