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                    Lidar/IMU緊耦合的實時定位方法

                    李帥鑫 李廣云 王力 楊嘯天

                    李帥鑫, 李廣云, 王力, 楊嘯天. Lidar/IMU緊耦合的實時定位方法. 自動化學報, 2020, 46(x): 1?13 doi: 10.16383/j.aas.c190424
                    引用本文: 李帥鑫, 李廣云, 王力, 楊嘯天. Lidar/IMU緊耦合的實時定位方法. 自動化學報, 2020, 46(x): 1?13 doi: 10.16383/j.aas.c190424
                    Li Shuai-Xin, Li Guang-Yun, WANG Li, YANG Xiao-Tian. Lidar/IMU tightly coupled real-time localization method. Acta Automatica Sinica, 2020, 46(x): 1?13 doi: 10.16383/j.aas.c190424
                    Citation: Li Shuai-Xin, Li Guang-Yun, WANG Li, YANG Xiao-Tian. Lidar/IMU tightly coupled real-time localization method. Acta Automatica Sinica, 2020, 46(x): 1?13 doi: 10.16383/j.aas.c190424

                    Lidar/IMU緊耦合的實時定位方法

                    doi: 10.16383/j.aas.c190424
                    基金項目: 地理信息工程國家重點實驗室基金(SKLGIE2018-M-3-1), 國家重點研發計劃(2017YFF0206001), 國家自然科學基金(41501491)資助
                    詳細信息
                      作者簡介:

                      李帥鑫:戰略支援部隊信息工程大學地理空間信息學院博士研究生. 2015年獲中南大學測繪工程學士學位, 2018年獲戰略支援部隊信息工程大學控制科學與工程碩士學位. 主要研究方向為多傳感器融合的SLAM, 移動測量. 本文通信作者. E-mail: lsx_navigation@sina.com

                      李廣云:戰略支援部隊信息工程大學地理空間信息學院教授、博導. 1987年獲解放軍測繪學院測繪科學與技術碩士學位. 主要研究方向為精密工程與工業測量, 導航應用及導航定位與位置服務. E-mail: guangyun_li@163.com

                    •  收稿日期?2019-06-02????錄用日期?2019-12-15 Manuscript?received?June?2,?2019;?accepted?December?15,?2019 地理信息工程國家重點實驗室基金 (SKLGIE2018-M-3-1),?國家重點研發計劃 (2017YFF0206001),?國家自然科學基金(41501491) 資助 Supported?by?State?Key?Laboratory?of?Geo-Information?Engineering(SKLGIE2018-M-3-1), National Key Research and Development Project£2017YFF0206001),?National?Natural?Science Foundation?of?China(41501491)
                    •  本文責任編委?吳毅紅 Recommended?by?Associate?Editor WU Yi-Hong 1.?地理信息工程國家重點實驗室?西安?710054????2.?戰略支援部隊信息工程大學地理空間信息學院?鄭州?450001 1. State Key Laboratory of Geo-Information Engineering, Xi’an 710054????2. College of Geospatial Information, PLA Information Engineering University, Zhengzhou 450001
                    • 1http://www.cvlibs.net/datasets/kitti/eval_odometry.php

                    Lidar/IMU Tightly Coupled Real-time Localization Method

                    Funds: Supported by State Key Laboratory of Geo-Information Engineering(SKLGIE2018-M-3-1), National Key Research and Development Project(2017YFF0206001), National Natural Science Foundation of China(41501491)
                    • 摘要: 本文以實現移動小型智能化系統的實時自主定位為目標, 針對激光里程計誤差累計大, 旋轉估計不穩定, 以及觀測信息利用不充分等問題, 提出一種Lidar/IMU緊耦合的實時定位方法—Inertial-LOAM. 數據預處理部分, 對IMU數據預積分, 降低優化變量維度, 并為點云畸變校正提供參考. 提出一種基于角度圖像的快速點云分割方法, 篩選結構性顯著的點作為特征點, 降低點云規模, 保證激光里程計的效率; 針對地圖構建部分存在的地圖匹配點搜索效率低和離散點云地圖的不完整性問題, 提出傳感器中心的多尺度地圖模型, 利用環形容器保持地圖點恒定, 并結合多尺度格網保證地圖模型中點的均勻分布. 數據融合部分, 提出Lidar/IMU緊耦合的優化方法, 將IMU和Lidar構成的預積分因子、配準因子、閉環因子插入全局因子圖中, 采用基于貝葉斯樹的因子圖優化算法對變量節點進行增量式優化估計, 實現數據融合. 最后, 采用實測數據評估Inertial-LOAM的性能并與LeGO-LOAM, LOAM和Cartographer對比. 結果表明, Inertial-LOAM在不明顯增加運算負擔的前提下大幅降低連續配準誤差造成的誤差累計, 具有良好的實時性; 在結構性特征明顯的室內環境, 定位精度達厘米級, 與對比方法持平; 在開闊的室外環境, 定位精度達分米級, 而對比方法均存在不同程度的漂移.
                      1)   收稿日期?2019-06-02????錄用日期?2019-12-15 Manuscript?received?June?2,?2019;?accepted?December?15,?2019 地理信息工程國家重點實驗室基金 (SKLGIE2018-M-3-1),?國家重點研發計劃 (2017YFF0206001),?國家自然科學基金(41501491) 資助 Supported?by?State?Key?Laboratory?of?Geo-Information?Engineering(SKLGIE2018-M-3-1), National Key Research and Development Project£2017YFF0206001),?National?Natural?Science Foundation?of?China(41501491)
                      2)   本文責任編委?吳毅紅 Recommended?by?Associate?Editor WU Yi-Hong 1.?地理信息工程國家重點實驗室?西安?710054????2.?戰略支援部隊信息工程大學地理空間信息學院?鄭州?450001 1. State Key Laboratory of Geo-Information Engineering, Xi’an 710054????2. College of Geospatial Information, PLA Information Engineering University, Zhengzhou 450001
                      3)  1http://www.cvlibs.net/datasets/kitti/eval_odometry.php
                    • 圖  1  系統框架圖

                      Fig.  1  The overview of the system

                      圖  2  點云分割示例

                      Fig.  2  Example of point cloud segmentation

                      圖  3  IMU與Lidar的頻率關系

                      Fig.  3  Frequencies of IMU and Lidar

                      圖  4  局部地圖示意圖

                      Fig.  4  Demonstration for the local map

                      圖  5  因子圖結構

                      Fig.  5  Structure of the factor graph

                      圖  6  數據采集平臺

                      Fig.  6  Data collection platform

                      圖  10  Inertial-LOAM軌跡及建圖結果

                      Fig.  10  Trajectory and mapping result of Inertial-LOAM

                      圖  7  系統運行時間對比

                      Fig.  7  Comparison of time cost of two systems

                      圖  8  閉環優化效果

                      Fig.  8  Performance of loop optimization

                      圖  9  室外開闊環境IL/LL/L/C軌跡結果對比

                      Fig.  9  Comparison of pose estimation of IL/LL/L/C in outdoor environment

                      表  1  累計誤差結果

                      Table  1  Error accumulation result

                      場景方法橫滾(°)俯仰(°)航向(°)角度偏差(°)X方向(m)Y方向(m)Z方向(m)位置偏差(m)
                      2#數據[11]IMU0.7481.0180.5981.39835.09584.652?665.782672.059
                      Cartographer0.113?0.7090.9891.2220.4051.3170.6701.532
                      LOAM0.0160.1410.9250.9360.3160.3490.0250.471
                      LeGO-LOAM0.0610.0810.9160.9210.0680.3380.1150.364
                      Inertial-LOAM0.0130.0260.9170.9180.0610.2580.0230.266
                      室內環境Cartographer0.003?0.0010.0170.0170.0230.0370.0280.052
                      LOAM0.0010.0040.0680.0680.0320.0830.0320.095
                      LeGO-LOAM?0.006?0.002?0.0210.0220.0160.047?0.0320.059
                      Inertial-LOAM?0.0080.001?0.0200.0210.0210.0430.0270.055
                      室外環境Cartographer0.075?0.0240.0810.1131.7472.592?0.4493.158
                      LOAM?0.0310.0060.0960.1010.04672.368?0.0652.353
                      LeGO-LOAM?0.024?0.5430.0410.545?19.857?14.914?0.35524.836
                      Inertial-LOAM0.006?0.0800.0030.080?0.310?0.100?0.0300.328
                      下載: 導出CSV
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                    • 收稿日期:  2019-06-02
                    • 錄用日期:  2019-12-15
                    • 網絡出版日期:  2020-01-16

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