基於語義型閉環檢測之機器人同步建圖與定位系統;Toward Semantic Loop Closure in Simultaneous Localization and Mapping Systems

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Title:	基於語義型閉環檢測之機器人同步建圖與定位系統;Toward Semantic Loop Closure in Simultaneous Localization and Mapping Systems
Authors:	楊哲宇;Yang, Che-Yu
Contributors:	通訊工程學系
Keywords:	環境辨識;閉環檢測;物件辨識;Place Recognition;Bag of words approach;Appearance-based localization and mapping;SLAM
Date:	2018-08-16
Issue Date:	2018-08-31 14:38:55 (UTC+8)
Publisher:	國立中央大學
Abstract:	即時定位與建圖系統SLAM(Simultaneous localization and mapping)是用來幫助機器人建立環境資訊同時估計自身的位姿的一種技術，被用來解決很多的新興應用，像是自駕車、無人載具、家庭式服務機器人等。可想而知，SLAM系統的精確度受到其光學設備影響非常的大，像是雷射、彩色相機、聲納等，皆會有不同的輸入訊號品質，進而影響著SLAM系統的效能。SLAM系統中很重要的一樣就是閉環檢測，其主要的功能是讓機器人識別出過去曾經到過的場景，來減小系統的累積誤差。在傳統的SLAM系統中，主要是利用幾何特徵來做識別，但是在有非常相似幾何特徵的場景中，其效果會下降非常的多。因此需要新的環境特徵來增加其效果，語義型物件特徵，即被用來當作全新的環境特徵，給予SLAM系統一個新種類的環境資訊來做識別。在此論文中，我們首先會先對SLAM系統做個介紹，再針對語義型物件方法提升分辨相似場景的能力做介紹。在室內環境中，有非常多的幾何上相似的場景出現，因此語義型物件特徵可以提供比幾何特徵更好的效果。 ;Simultaneous localization and mapping (SLAM) is a problem in robotics aiming to model the environment and estimate the pose of a device within it at the same time. Developed solution is the core technology for emerging applications such as self-driving cars, automated guided vehicles (AGV), and domestic robots. Inevitably, the performance of SLAM algorithms relies highly on input signals from optical equipment ranging from cameras, laser rangefinders, and LIDAR. Loop closure, the function detecting visited locations to correct accumulated errors, is a crucial element in a SLAM system. Conventionally, geometric features are used to interpret the scenes for similarity estimation. In scenarios with nearly identical scenes existing, the feature-based approaches remain ineffective. Semantic objects, therefore, can be integrated into the process and present a new level of environmental information. In this article, we first provide an overview of the SLAM system. Then a semantic object-assisted approach is proposed to improve the similarity measurement in the SLAM process. By integrating recognized objects like landmarks and signs, we can classify similar scenes better and significantly improve building-scale indoor mapping results.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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