2021/01/24 by Sohum Misra, Kaarthik Sundar, Misra, Sohum +5
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Indoor and Outdoor Localization Technologies #Optimization and Control (math.OC) #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization #cs.RO #math.OC
paper · pdf · doi:10.48550/arxiv.2101.09750
37 pages
openalex publication_date 2021/01/24 · arxiv created 2021/11/02 · arxiv updated 2021/11/04 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
This paper presents a novel data-driven navigation system to navigate an Unmanned Vehicle (UV) in GPS-denied, feature-deficient environments such as tunnels, or mines. The method utilizes landmarks that vehicle can deploy and measure range from to enable localization as the vehicle traverses its pre-defined path through the tunnel. A key question that arises in such scenario is to estimate and reduce the number of landmarks that needs to be deployed for localization before the start of the mission, given some information about the environment. The main focus is to keep the maximum position uncertainty at a desired value. In this article, we develop a novel vehicle navigation system in GPS-denied, feature-deficient environment by combining techniques from estimation, machine learning, and mixed-integer convex optimization. This article develops a novel, systematic method to perform localization and navigate the UV through the environment with minimum number of landmarks while maintaining desired localization accuracy. We also present extensive simulation experiments on different scenarios that corroborate the effectiveness of the proposed navigation system.