针对视觉惯性里程计(Visual-Inertial Odometry, VIO)在越野环境中定位性能显著下降的问题,本文提出了一种基于轮速里程计与VIO紧耦合的算法HW-VIO (Hybrid Wheel-VIO)。该算法融合了IMU与轮速里程计的特点,设计了混合预积分观测模型,...针对视觉惯性里程计(Visual-Inertial Odometry, VIO)在越野环境中定位性能显著下降的问题,本文提出了一种基于轮速里程计与VIO紧耦合的算法HW-VIO (Hybrid Wheel-VIO)。该算法融合了IMU与轮速里程计的特点,设计了混合预积分观测模型,并利用轮速里程计的零速更新校正IMU加速度计和陀螺仪的偏置误差。为改善轮速计异常值频发的问题,本文引入卡方检验算法,对混合预积分残差进行评估,从而稳健识别并剔除异常数据。最后,在三种难度不同的野外农田场景中对算法进行了测试。实验结果表明,本文算法能够显著提高VIO系统的性能,平均定位精度提升47%。此外,通过消融实验进一步验证了混合预积分观测模型的有效性,相较于直接进行轮速融合的W-VIO (Wheel-VIO)算法,平均定位精度提升达50%。The declining localization performance of Visual-Inertial Odometry (VIO) in off-road environments is a significant challenge. To address this issue, a tightly coupled algorithm named HW-VIO (Hybrid Wheel-VIO) is proposed, combining wheel odometry and VIO. The method leverages the complementary properties of IMU and wheel odometry by introducing a hybrid pre-integration observation model, where zero-velocity updates from wheel odometry are employed to dynamically correct accelerometer and gyroscope biases in the IMU. To handle the frequent occurrence of outliers in wheel odometry measurements, a chi-squared test is applied to evaluate residuals from the hybrid pre-integration process, enabling robust identification and rejection of abnormal data. The algorithm is validated through experiments conducted in three off-road farmland scenarios with varying levels of difficulty. Results show that HW-VIO significantly improves localization accuracy, achieving an average accuracy improvement of 47%. Furthermore, ablation studies confirm the effectiveness of the hybrid pre-integration model, demonstrating a 50% improvement in localization accuracy compared to the W-VIO (Wheel-VIO) algorithm, which directly fuses wheel odometry.展开更多
PPP-RTK(precise point positioning real time kinematic)是一种具有潜力的定位技术,它既避免了RTK(real time kinematic)覆盖范围受限的缺陷,又解决了PPP(precise point positioning)收敛速度慢的问题。但在城市复杂环境下,由于信号...PPP-RTK(precise point positioning real time kinematic)是一种具有潜力的定位技术,它既避免了RTK(real time kinematic)覆盖范围受限的缺陷,又解决了PPP(precise point positioning)收敛速度慢的问题。但在城市复杂环境下,由于信号遮挡严重,PPP-RTK无法实现高精度连续定位。惯性导航(inertial navigation system,INS)和视觉导航能提供连续的定位信息,但存在误差漂移,由此提出多系统PPP-RTK/VIO(visual inertial odometry)半紧组合算法,并在武汉大学校园内采集车载数据进行验证。实验结果显示,多系统PPPRTK/VIO半紧组合在定位表现上相比于GPS(global positioning system)+BDS(BeiDou navigation satellite system),PPP-RTK能带来超过30%的精度提升,达到平面0.58 m,高程1.12 m。多系统PPP-RTK/VIO半紧组合的测速和姿态估计性能也较好,测速精度在北向、东向和地向分别达到0.04 m/s、0.04 m/s和0.02 m/s,横滚角、俯仰角和航向角估计精度分别达到0.10°、0.06°和0.17°。展开更多
By means of ultra-violet(UV)irradiation with photoini-tiator and multifunctional crosslinking agent,thecrosslinking modification of ultrahigh molecular weightpolyethylene(UHMWPE)fibers prepared by gel-spin-ning was ca...By means of ultra-violet(UV)irradiation with photoini-tiator and multifunctional crosslinking agent,thecrosslinking modification of ultrahigh molecular weightpolyethylene(UHMWPE)fibers prepared by gel-spin-ning was carried out.Thermal properties of fiber sampleswere examined using differential scanning calorimetry(DSC),thermomechanical analysis(TMA)apparatusand a manual device.The results indicated that the opti-mal irradiation energy is 250-400 mJ/cm^2,heat-andcreep-resistant behaviors of modified fibers have beenimproved.展开更多
文摘针对视觉惯性里程计(Visual-Inertial Odometry, VIO)在越野环境中定位性能显著下降的问题,本文提出了一种基于轮速里程计与VIO紧耦合的算法HW-VIO (Hybrid Wheel-VIO)。该算法融合了IMU与轮速里程计的特点,设计了混合预积分观测模型,并利用轮速里程计的零速更新校正IMU加速度计和陀螺仪的偏置误差。为改善轮速计异常值频发的问题,本文引入卡方检验算法,对混合预积分残差进行评估,从而稳健识别并剔除异常数据。最后,在三种难度不同的野外农田场景中对算法进行了测试。实验结果表明,本文算法能够显著提高VIO系统的性能,平均定位精度提升47%。此外,通过消融实验进一步验证了混合预积分观测模型的有效性,相较于直接进行轮速融合的W-VIO (Wheel-VIO)算法,平均定位精度提升达50%。The declining localization performance of Visual-Inertial Odometry (VIO) in off-road environments is a significant challenge. To address this issue, a tightly coupled algorithm named HW-VIO (Hybrid Wheel-VIO) is proposed, combining wheel odometry and VIO. The method leverages the complementary properties of IMU and wheel odometry by introducing a hybrid pre-integration observation model, where zero-velocity updates from wheel odometry are employed to dynamically correct accelerometer and gyroscope biases in the IMU. To handle the frequent occurrence of outliers in wheel odometry measurements, a chi-squared test is applied to evaluate residuals from the hybrid pre-integration process, enabling robust identification and rejection of abnormal data. The algorithm is validated through experiments conducted in three off-road farmland scenarios with varying levels of difficulty. Results show that HW-VIO significantly improves localization accuracy, achieving an average accuracy improvement of 47%. Furthermore, ablation studies confirm the effectiveness of the hybrid pre-integration model, demonstrating a 50% improvement in localization accuracy compared to the W-VIO (Wheel-VIO) algorithm, which directly fuses wheel odometry.
文摘PPP-RTK(precise point positioning real time kinematic)是一种具有潜力的定位技术,它既避免了RTK(real time kinematic)覆盖范围受限的缺陷,又解决了PPP(precise point positioning)收敛速度慢的问题。但在城市复杂环境下,由于信号遮挡严重,PPP-RTK无法实现高精度连续定位。惯性导航(inertial navigation system,INS)和视觉导航能提供连续的定位信息,但存在误差漂移,由此提出多系统PPP-RTK/VIO(visual inertial odometry)半紧组合算法,并在武汉大学校园内采集车载数据进行验证。实验结果显示,多系统PPPRTK/VIO半紧组合在定位表现上相比于GPS(global positioning system)+BDS(BeiDou navigation satellite system),PPP-RTK能带来超过30%的精度提升,达到平面0.58 m,高程1.12 m。多系统PPP-RTK/VIO半紧组合的测速和姿态估计性能也较好,测速精度在北向、东向和地向分别达到0.04 m/s、0.04 m/s和0.02 m/s,横滚角、俯仰角和航向角估计精度分别达到0.10°、0.06°和0.17°。
基金oral prepared in the Second East Asian Polymer Conference held in Hongkong,China,January 12-16,1999
文摘By means of ultra-violet(UV)irradiation with photoini-tiator and multifunctional crosslinking agent,thecrosslinking modification of ultrahigh molecular weightpolyethylene(UHMWPE)fibers prepared by gel-spin-ning was carried out.Thermal properties of fiber sampleswere examined using differential scanning calorimetry(DSC),thermomechanical analysis(TMA)apparatusand a manual device.The results indicated that the opti-mal irradiation energy is 250-400 mJ/cm^2,heat-andcreep-resistant behaviors of modified fibers have beenimproved.
