Summary of Ai and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems, by Sudeep Pasricha
AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems
by Sudeep Pasricha
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores the challenges faced by current indoor navigation solutions, which rely on ubiquitous wireless signals and sensors to track and localize humans, autonomous vehicles, drones, and robots in various environments. To overcome these challenges, AI algorithms are deployed on mobile embedded systems to improve indoor navigation capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Indoor navigation is important for tracking people, cars, drones, and robots inside buildings, underground, or dense cities where GPS signals don’t work well. Right now, we use WiFi and sensors in devices like smartphones to navigate indoors. This paper talks about the problems with current solutions and how artificial intelligence (AI) can help fix them. |
Keywords
* Artificial intelligence * Tracking
