Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.

Heading constraint algorithm for foot-mounted PNS using low-cost IMU

Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the traditional algorithm,such as the accumulated errors and the lack of observation of heading and altitude information,have become obstacles to the application and development of the PNS.In this paper,we introduce a heuristic heading constraint method.First of all,according to the movement characteristics of human gait,we use the generalized likelihood ratio test(GLRT)detector and introduce a time threshold to classify the human gait,so that we can effectively identify the stationary state of the foot.In addition,based on zero velocity update(ZUPT)and zero angular rate update(ZARU),the cumulative error of the inertial measurement unit(IMU)is limited and corrected,and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian.After simulation and experiments with low-cost IMU,the method is proved to reduce the localization error of end-point to less than 1%of the total distance,and it has great value in application.

Integration of GPS and low cost INS for pedestrian navigation aided by building layout

In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pedestrian navigation for these environments should be realized by the integration of GPS and inertial navigation system （INS）. However, the lowcost INS could induce errors that may result in a large position drift. The problem can be minimized by mounting the sensors on the pedestrian＇s foot, using zero velocity update （ZUPT） method with the standard navigation algorithm to restrict the error growth. However, heading drift still remains despite using ZUPT measurements since the heading error is unobservable. Also, tbot mounted INS suffers from the initialization ambiguity of position and heading from GPS. In this paper, a novel algorithm is developed to mitigate the heading drift problem when using ZUPT. The method uses building lay- out to aid the heading measurement in Kalman filter, and it could also be combined for the initial- ization. The algorithm has been investigated with real field trials using the low cost Microstrain 3DM-GX3-25 inertial sensor, a Leica GS10 GPS receiver and a uBlox EVK-6T GPS receiver. It could be concluded that the proposed method offers a significant improvement in position accuracy for the long period, allowing pedestrian navigation for nearly40 min with mean position error less than 2.8 m. This method also has a considerable effect on the accuracy of the initialization.

Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS

Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applies this concept to walking persons.The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered.Here,the movement of a foot and the corresponding direction change is measured and summed up,to infer the current position.Measuring and integrating the corresponding physical parameters,e.g.using inertial sensors,introduces small errors that accumulate quickly into large distance errors.Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths.In this paper,we use building maps to improve localization based on a single foot-mounted inertial sensor.We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation.Even though the computation of individual steps is quite accurate,small errors still accumulate in the long term.We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks,such as orientation and displacement drift,and discuss restrictions and disadvantages of these algorithms.We also present a method of deriving the initial position and orientation from GPS measurements.We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building,walking each route three times.Our quantitative results show an endpoint accuracy improvement of up to 60%when using likely paths and 23%when using unlikely paths.However,both approaches can also decrease accuracy in certain scenarios.We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.