Method of improving pedestrian navigation performance based on chest card

With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.The pedestrian naviga-tion based on radio is subject to environmental occlusion lead-ing to the degradation of positioning accuracy.The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit(MIMU)is less susceptible to environmental interference,but its errors dissipate over time.In this paper,a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods.To suppress atti-tude errors,optimal feedback coefficients are established by pedestrian motion characteristics.To extend navigation time and improve positioning accuracy,the step length in subsequent movements is compensated by the first step length.The experi-mental results show that the positioning accuracy of the pro-posed method is improved by more than 47%and 44%com-pared with the pure inertia-based method combined with step compensation and the traditional complementary filtering com-bined method with step compensation.The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.

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.

What about people in pedestrian navigation?

Pedestrian navigation has become an important theoretical and practical research topic in many disciplines such as cartography,geographical information science,global and indoor positioning,spatial behavior,psychology,sociology,and neuroscience.Many research studies view pedestrian navigation using process-oriented and goal-directed approaches.However,this paper revisits people’s needs in pedestrian navigation and classifies their needs as three layers:physical sense layer,physiological safety layer,and mental satisfaction layer according to Maslow’s theory.This paper introduces a people-centric framework for pedestrian navigation theory based on these three layers and discusses theoretical challenges for meeting each layer of people’s needs.These challenging theories may represent promising and valuable research and promote usage of pedestrian navigation systems or devices in the future.

Detecting visually salient scene areas and deriving their relative spatial relations from continuous street-view panoramas

A salient scene is an area within an image that contains visual elements that stand out from surrounding areas.They are important for distinguishing landmarks in first-person-view(FPV)applications and determining spatial relations in images.The relative spatial relation between salient scenes acts as a visual guide that is easily accepted and understood by users in FPV applications.However,current digitally navigable maps and location-based services fall short of providing information on visual spatial relations for users.This shortcoming has a critical influence on the popularity and innovation of FPV applications.This paper addresses the issue by proposing a method for detecting visually salient scene areas(SSAs)and deriving their relative spatial relationships from continuous panoramas.This method includes three critical steps.First,an SSA detection approach is introduced by fusing region-based saliency derived from super-pixel segmentation and the frequency-tuned saliency model.The method focuses on a segmented landmark area in a panorama.Secondly,a street-view-oriented SSA generation method is introduced by matching and merging the visual SSAs from continuous panoramas.Thirdly,a continuous geotagged panorama-based referencing approach is introduced to derive the relative spatial relationships of SSAs from continuous panoramas.This information includes the relative azimuth,elevation angle,and the relative distance.Experiment results show that the error for the SSA relative azimuth angle is approximately±6°(with an average error of 2.67°),and the SSA relative elevation angle is approximately±4°(with an average error of 1.32°)when using Baidu street-view panoramas.These results demonstrate the feasibility of the proposed approach.The method proposed in this study can facilitate the development of FPV applications such as augmented reality(AR)and pedestrian navigation using proper spatial relation.