Tackling Heterogeneous Light Detection and Ranging-Camera Alignment Challenges in Dynamic Environments: A Review for Object Detection
Yujing Wang, Abdul Hadi Abd Rahman, Fadilla ’Atyka Nor Rashid, Mohamad Khairulamirin Md Razali, Jesper Skottfelt · 2024
Read Tackling Heterogeneous Light Detection and Ranging-Camera Alignment Challenges in Dynamic Environments: A Review for Object Detection by Yujing Wang, Abdul Hadi Abd Rahman, Fadilla ’Atyka Nor Rashid, Mohamad Khairulamirin Md Razali, Jesper Skottfelt (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11645047.txt.
Description
Yujing Wang and co-authors review, in 2024, the hard problem of aligning laser range data with camera images for spotting objects. Cars, robots, and augmented reality tools pair these two sensors, yet their data differ in format, detail, and response to weather. In moving scenes, lining them up in space and time is harder still.
The review compares more than twenty alignment methods from recent years for three-dimensional object detection. It sorts the methods, weighs their strengths and limits, and names open problems such as sensor fusion, scale, and real-time demands. Earlier reviews focused on fusion math, so alignment gets overdue attention here.
Catalog record
| CREATOR | Yujing Wang, Abdul Hadi Abd Rahman, Fadilla ’Atyka Nor Rashid, Mohamad Khairulamirin Md Razali, Jesper Skottfelt |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |