基於極線限制條件之單眼視覺式移動物體偵測與追蹤

Moving Object Detection and Tracking Using Monocular Vision Based on Epipolar Constraints

邱明璋(Ming-Jang Chiou)

指導教授 ： 王銀添

淡江大學/工學院/機械與機電工程學系碩士班/碩士(2011年)

https://doi.org/10.6846/TKU.2011.00602

摘要

本論文使用擴張型卡爾曼濾波器(EKF)發展機器人的視覺式同時自我定位、建圖、與移動物體追蹤(SLAMMOT)系統。論文分為兩個部份：第一部份使用單眼視覺為唯一感測器，並以影像修正取代攝影機校正，簡化狀態估測時量測模型線性化的推導程序。第二部份以靜態影像特徵在極線上的限制條件之概念，發展在影像平面上偵測移動物體的演算法。並針對移動物體規劃資料關聯程序、以及移動物體特徵管理的新增與刪除策略。所發展的演算法最後整合成為單眼視覺式EKF SLAMMOT系統，也在室內環境中成功測試路徑閉合的功能、長距離SLAM任務的能力、移動物體偵測與追蹤的功能、以及比較與地面基準的誤差。

關鍵字

同時定位建圖與移動物體追蹤(SLAMMOT) ； 極線限制條件 ； 機器人視覺 ； 擴張型卡爾曼過濾器(EKF)

並列摘要

In this thesis, the visual simultaneous localization, mapping and moving object tracking (SLAMMOT) is developed using the extended Kalman filter (EKF). The research is divided into two parts: first, one monocular vision is utilized as the only sensor for the SLAMMOT system. The camera calibration is replaced by an image correction model to simplify the linearization derivation of the measurement equation in state estimation. Second, the algorithm of moving object detection (MOD) is developed based on the constraint condition of static image features on the epipolar line. We also develop the procedures of data association and map management for the SLAM task with moving objects. Finally, the EKF SLAMMOT with the proposed algorithm is implemented on a monocular vision system. The integrated system has successfully tested the basic capabilities of SLAM, including loop-closure, ground truth, long-distance navigation, and moving object detection and tracking for the system in the indoor environment.

並列關鍵字

Simultaneous Localization,Mapping and Moving Object Tracking (SLAMMOT) ； Constraint Condition of Epipolar Line ； Robot Vision ； Extended Kalman Filter (EKF)

參考文獻

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