基於車聯網架構下之自我定位技術

Abstract

車聯網 (Internet-of-Vehicle; IoV) 的發展與應用，對於改善行車之安 全及品質是可預期的。藉由車輛與車輛 (V2V) 以及車輛與基礎設施 (V2I) 的通訊，為駕駛人提供更完整並且無死角的路況資訊。為了確保 車輛間傳遞的訊息的可靠性，精確的車輛定位則成為了必要的條件， 也是本論文主要探討之研究主題。本文提出一種基於影像來作為車聯 網中車輛定位之系統架構，將行車記錄器所攝錄之影像傳送至路邊設 立之資料庫，並依資料庫中已建立之場景三維點雲模型回傳至車輛做 定位。為了降低資料庫記憶體成本及通訊開銷，以及解決場景因時間 或天氣的光線變化，本文亦提出了點雲模型壓縮及更新之演算法。

This paper presents a method for ego-positioning with low cost monocular cameras for an IoV (Internet-of-Vehicle) system. To reduce the computational and memory requirements as well as the communication overheads, we formulate the model compression algorithm as a weighted k-cover problem for better preserving model structures. Specifically for real-world vision-based positioning applications, we consider the issues with large scene change and propose a model update algorithm to tackle these problems. A long-term positioning dataset with more than one month, 105 sessions, and 14,167 images is constructed. Based on both local and up-to-date models constructed in our approach, extensive experimental results show that sub-meter positioning accuracy can be achieved, which outperforms existing vision-based algorithms.