INS/GPS/Odometer组合系统初始对准及自适应联合滤波

本文提出了 INS/GPS/Odometer(测速仪 )组合导航系统。基于信息融合理论 ,提出了基于子滤波器可观性矩阵条件数和误差协方差矩阵特征值分解的自适应联合滤波。然后利用该方法对 INS/GPS/Odometer组合系统的初始对准问题进行了研究。仿真结果表明 ,自适应联合滤波能够改善系统初始对准的状态估计精度 。

附加速度先验信息的车载GPS/INS/Odometer组合导航算法

在GPS/INS车辆组合导航中,GPS信号易受外界干扰而失锁。针对INS单独导航误差迅速累积的问题,在利用速度先验信息辅助INS导航的基础上,加入Odometer观测信息,提高了系统的可观测性和导航精度;另外提出了改进的位置修正法,即不直接利用状态估值修正位置,而是用修正后的速度推算位置。实测算例结果表明,与INS单独导航相比较,采用速度先验信息,提高了载体速度精度,采用改进的位置修正方法,位置精度有大幅度提高;在此基础上加入Odometer观测信息,位置和速度精度得到进一步改善。

Capsule-odometer: A concept to improve accurate lesion localisation

In order to improve lesion localisation in small-bowel capsule endoscopy,a modified capsule design has been proposed incorporating localisation and-in theorystabilization capabilities.The proposed design consists of a capsule fitted with protruding wheels attached to a spring-mechanism.This would act as a miniature odometer,leading to more accurate lesion localization information in relation to the onset of the investigation(spring expansion e.g.,pyloric opening).Furthermore,this capsule could allow stabilization of the recorded video as any erratic,non-forward movement through the gut is minimised.Three-dimensional(3-D)printing technology was used to build a capsule prototype.Thereafter,miniature wheels were also 3-D printed and mounted on a spring which was attached to conventional capsule endoscopes for the purpose of this proof-of-concept experiment.In vitro and ex vivo experiments with porcine small-bowel are presented herein.Further experiments have been scheduled.

Grain crushing and its effects on rheological behavior of weathered granular soil

To disclose the grain crushing effects on the weathered granular soil rheological behavior,a series of rheological tests (odometer compression and triaxial shearing) were carried out.At the same time,the sieving analysis tests of these specimens were also executed before and after tests,and the grain crushing degree,Br and n5,were collectively adopted to estimate the grain crushing.The grain crushing degree depends on the stress path,stress level,and load time,especially,the longer load time and more intensive gradient shearing path will increase the grain crushing quantity.The Hardin crushing degrees Br are 0.191,0.118 and 0.085 in the ordinary compression,rheological compression and triaxial rheological shearing,respectively;The grain crushing degrees n5 are 1.9,1.4 and 1.32,respectively.The strain softening phase indicates the grain crushing and diffusive collapse,and the strain hardening phase indicates the rearrangement of these crushed grains and formation of new bearing soil skeleton.The rheological deformation of granular soil can be attributed to the coarse grain crushing and the filling external porosity with crushed fragments.

Localization and mapping algorithm based on Lidar-IMU-Camera fusion

Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems.In a complex traffic environment,the signal of the Global Navigation Satellite System(GNSS)will be blocked,leading to inaccurate vehicle positioning.To ensure the security of automatic electric campus vehicles,this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain(LEGO-LOAM)algorithm with a monocular vision system added.An algorithm framework based on Lidar-IMU-Camera(Lidar means light detection and ranging)fusion was proposed.A lightweight monocular vision odometer model was used,and the LEGO-LOAM system was employed to initialize monocular vision.The visual odometer information was taken as the initial value of the laser odometer.At the back-end opti9mization phase error state,the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning.The visual word bag model was applied to perform loopback detection.Taking the test results into account,the laser radar loopback detection was further optimized,reducing the accumulated positioning error.The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment.The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav.Compared with the LEGO-LOAM algorithm,the results show that the proposed algorithm can effectively reduce map drift,improve map resolution,and output more accurate driving trajectory information.

Deriving Network-Constrained Trajectories from Sporadic Tracking Points Collected in Location-Based Services

The paper proposes an economical and fast algorithm for deriving trajectories from sporadic tracking points collected in location-based services (LBS). Although many traffic studies or applications can benefit from the derived trajectories, the sporadic tracking points are always implicitly overlooked by most of existing map-matching algorithms. The algorithm proposed in this paper finds network paths or trajectories traveled by vehicles through augmenting GPS data with odometer data. An odometer can provide data of traveled distance which are compared with the lengths of candidate network paths in order to find the most approximate network path approaching the trajectory of a vehicle. Tracking points are classified into anchor points and non-anchor points. The former are used to divide trajectories, and the latter screen candidate network paths. An elliptic selection zone and a reduction process are applied to the selection of possible road segments composing candidate network paths. A brute-force searching algorithm is developed to find candidate network paths and calculate their lengths. A two-step screening process is designed to select the final result from candidate network paths. Finally, a series of experiments are conducted to validate the proposed algorithm.