作为天津一汽丰田的开山之作,威驰(T-1)一直是丰田在中国的入门车型。从当年接近20万元的封顶价,到现在9.49万元的起步价;从国产丰田品牌形象的树立,到现在价格品质比的杀手,丰田的策略无疑是成功的。眼前的这部威驰1.5 AT GLX-i高级音...作为天津一汽丰田的开山之作,威驰(T-1)一直是丰田在中国的入门车型。从当年接近20万元的封顶价,到现在9.49万元的起步价;从国产丰田品牌形象的树立,到现在价格品质比的杀手,丰田的策略无疑是成功的。眼前的这部威驰1.5 AT GLX-i高级音响版。展开更多
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
Visual inertial odometry(VIO)problems have been extensively investigated in recent years.Existing VIO methods usually consider the localization or navigation issues of robots or autonomous vehicles in relatively small...Visual inertial odometry(VIO)problems have been extensively investigated in recent years.Existing VIO methods usually consider the localization or navigation issues of robots or autonomous vehicles in relatively small areas.This paper considers the problem of vision-aided inertial navigation(VIN)for aircrafts equipped with a strapdown inertial navigation system(SINS)and a downward-viewing camera.This is different from the traditional VIO problems in a larger working area with more precise inertial sensors.The goal is to utilize visual information to aid SINS to improve the navigation performance.In the multistate constraint Kalman filter(MSCKF)framework,we introduce an anchor frame to construct necessary models and derive corresponding Jacobians to implement a VIN filter to directly update the position in the Earth-centered Earth-fixed(ECEF)frame and the velocity and attitude in the local level frame by feature measurements.Due to its filtering-based property,the proposed method is naturally low computational demanding and is suitable for applications with high real-time requirements.Simulation and real-world data experiments demonstrate that the proposed method can considerably improve the navigation performance relative to the SINS.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
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.
基金supported by the National Natural Science Foundation of China(61773306).
文摘Visual inertial odometry(VIO)problems have been extensively investigated in recent years.Existing VIO methods usually consider the localization or navigation issues of robots or autonomous vehicles in relatively small areas.This paper considers the problem of vision-aided inertial navigation(VIN)for aircrafts equipped with a strapdown inertial navigation system(SINS)and a downward-viewing camera.This is different from the traditional VIO problems in a larger working area with more precise inertial sensors.The goal is to utilize visual information to aid SINS to improve the navigation performance.In the multistate constraint Kalman filter(MSCKF)framework,we introduce an anchor frame to construct necessary models and derive corresponding Jacobians to implement a VIN filter to directly update the position in the Earth-centered Earth-fixed(ECEF)frame and the velocity and attitude in the local level frame by feature measurements.Due to its filtering-based property,the proposed method is naturally low computational demanding and is suitable for applications with high real-time requirements.Simulation and real-world data experiments demonstrate that the proposed method can considerably improve the navigation performance relative to the SINS.
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°。
