發(fā)布時間：2018-05-03 21:27

  本文選題：同時定位和地圖構(gòu)建 + 室內(nèi)機器人　； 參考：《計算機應(yīng)用》2017年05期

【摘要】：針對在室內(nèi)機器人定位和三維稠密地圖構(gòu)建系統(tǒng)中,現(xiàn)有方法無法同時滿足高精度定位、大范圍和快速性要求的問題,應(yīng)用具有跟蹤、地圖構(gòu)建和重定位三平行線程的ORB-SLAM算法估計機器人三維位姿;然后拼接深度攝像頭KINECT獲得的三維稠密點云,提出空間域上的關(guān)鍵幀提取方法剔除冗余的視頻幀;接著提出子地圖法進一步減少地圖構(gòu)建的時間,最終提高算法的整體速度。實驗結(jié)果表明,所提系統(tǒng)能夠在大范圍環(huán)境中準確定位機器人位置,在運動軌跡為50 m的大范圍中,機器人的均方根誤差為1.04 m,即誤差為2%,同時整體速度為11幀/秒,其中定位速度達到17幀/秒,可以滿足室內(nèi)機器人定位和三維稠密地圖構(gòu)建的精度、大范圍和快速性的要求。
[Abstract]:In order to solve the problem that the existing methods can not meet the requirements of high accuracy, large range and fast in the indoor robot localization and 3D dense map construction system, the application has tracking. The ORB-SLAM algorithm of map construction and resetting three parallel threads is used to estimate the position and pose of the robot, and then the 3D dense point cloud obtained by the depth camera KINECT is spliced together, and the key frame extraction method in spatial domain is proposed to remove redundant video frames. Then submap method is proposed to further reduce the time of map construction, and finally improve the overall speed of the algorithm. The experimental results show that the proposed system can accurately locate the position of the robot in a wide range of environments. In a large range of motion tracks of 50 m, the root-mean-square error of the robot is 1.04 m, that is, the error is 2 parts, and the overall speed is 11 frames / s. The speed of localization can reach 17 frames / s, which can meet the requirements of accuracy, wide range and rapidity of indoor robot localization and 3D dense map construction.
【作者單位】： 華南理工大學(xué)自動化科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61573148) 廣東省科技重大專項(2015B010919007)~~
【分類號】：TP242
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本文編號：1840173