The current inertial measurement unit(IMU)and odometry fusion navigation algorithms often incorporate non-holonomic constraints(NHC)to obtain three-dimensional velocity in the navigation frame.However,due to the integ...The current inertial measurement unit(IMU)and odometry fusion navigation algorithms often incorporate non-holonomic constraints(NHC)to obtain three-dimensional velocity in the navigation frame.However,due to the integral nature of the dead reckoning algorithm,the attitude errors of the IMU accumulate over time,causing the velocity transformation results to fail to accurately reflect the threedimensional velocity in the navigation frame.Based on the fact that during a vehicle's horizontal and uniform motion,the vertical acceleration is consistent with gravitational acceleration,this paper proposes an IMU/odometry fusion navigation algorithm based on horizontal attitude constraints(HAC).Building on non-holonomic constraints,this algorithm determines the motion state of the vehicle through accelerometer output and zeroes out the pitch and roll angles during horizontal and uniform motion.Verified through two sets of real-world vehicle test data,this algorithm improves horizontal positioning accuracy by approximately 63%and 70%,and vertical positioning accuracy by 98%and 97%,compared with the traditional NHC IMU/odometer fusion algorithm.展开更多
室内低/弱纹理、光照不足的场景下,视觉惯导融合的即时定位与建图(simultaneous localization and mapping,SLAM)定位精度明显优于纯视觉SLAM方法。然而,当前基于点特征的视觉惯导SLAM方法通常难以检测并追踪足够的特征,同时惯性测量单...室内低/弱纹理、光照不足的场景下,视觉惯导融合的即时定位与建图(simultaneous localization and mapping,SLAM)定位精度明显优于纯视觉SLAM方法。然而,当前基于点特征的视觉惯导SLAM方法通常难以检测并追踪足够的特征,同时惯性测量单元的先验测量信息亦未充分利用,导致SLAM整体定位精度低、鲁棒性弱。针对这些问题,构建一种自适应点线特征和惯性测量单元(inertial measurement unit,IMU)耦合的视觉SLAM方法。首先设计一种自适应的快速角点特征检测算法,以增强图像特征点检测的鲁棒性。另外,快速线特征检测算法易检测短线、断线,且图像因光照变化易导致线特征的“过提取”或“错提取”。因此,利用边缘检测二值图像构造自适应线特征提取算法,并借助消影点的特性筛选聚类线特征。然后,由点线特征重投影误差和IMU先验预积分位姿估计量,通过松耦合为SLAM前端位姿估计和算法提供稳健的初始化结果。随后,后端利用紧耦合建立视觉和IMU观测量的统一非线性最小化残差函数,并优化得到准确的图像帧位姿。最后,在开源数据集上测试验证,并对比几种经典SLAM方法。实验结果表明,所构建的SLAM方法平均定位精度至少提高12%,同时具有较强的鲁棒性。展开更多
基金from the National Key Research and Development Program project"Adaptive Navigation Software and Hardware Technology(2018YFB0505200)."。
文摘The current inertial measurement unit(IMU)and odometry fusion navigation algorithms often incorporate non-holonomic constraints(NHC)to obtain three-dimensional velocity in the navigation frame.However,due to the integral nature of the dead reckoning algorithm,the attitude errors of the IMU accumulate over time,causing the velocity transformation results to fail to accurately reflect the threedimensional velocity in the navigation frame.Based on the fact that during a vehicle's horizontal and uniform motion,the vertical acceleration is consistent with gravitational acceleration,this paper proposes an IMU/odometry fusion navigation algorithm based on horizontal attitude constraints(HAC).Building on non-holonomic constraints,this algorithm determines the motion state of the vehicle through accelerometer output and zeroes out the pitch and roll angles during horizontal and uniform motion.Verified through two sets of real-world vehicle test data,this algorithm improves horizontal positioning accuracy by approximately 63%and 70%,and vertical positioning accuracy by 98%and 97%,compared with the traditional NHC IMU/odometer fusion algorithm.
