使用神經輻射場進行同時定位與地圖建構

Abstract

這篇論文收集了我對神經輻射場 (NeRF) 和同步定位與映射 (SLAM) 的經驗和見解，並涵蓋了一篇提交給機器人會議的論文。我們提出了適用於spatial AI 的 Orbeez-SLAM。

一種可以通過視覺信號執行複雜任務並與人類合作的spatial AI是值得期待的。為了實現這一點，我們需要一個無需預訓練即可輕鬆適應新場景並實時為下游任務生成密集地圖的視覺SLAM。在這項工作中，我們開發了一個名為 Orbeez-SLAM 的視覺 SLAM，它成功地與 NeRF 和視覺里程計合作來實現我們的目標。此外，Orbeez-SLAM 可以與單目相機配合使用，因為它只需要 RGB 輸入，使其廣泛適用於真實世界。

This thesis collects my experiences and insights about the Neural Radiance Field (NERF) and Simultaneous Localization and Mapping (SLAM) and covers a paper submitted to a robotic conference. We propose Orbeez-SLAM applicable to spatial AI.

A spatial AI that can perform complex tasks through visual signals and cooperate with humans is anticipated. To achieve this, we need a visual SLAM that easily adapts to new scenes without pre-training and generates dense maps for downstream tasks in real-time. In this work, we develop a visual SLAM named Orbeez-SLAM, which successfully collaborates with NeRF and visual odometry to achieve our goals. Moreover, Orbeez-SLAM can work with the monocular camera since it only needs RGB inputs, making it widely applicable to the real world.