Indoor Pedestrian Navigation Based on Conditional Random Field Algorithm

Ren, Mingrong; Guo, Hang; Shi, Jingjing; Meng, Juan

doi:10.3390/mi8110320

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…In these cases, a cellular automata model is chosen, the floor is discretized at fixed size intervals, normally in grid or voxel-grid. The regular discretization allows a more precise location of people in the environment (Ren et al, 2017). Zhang et al (2015) assign weights to a floor grid, related to distance to exit or dangerous zones, to calculate the movement of a crowd in an emergency situation.…”

Section: Related Workmentioning

confidence: 99%

Point Clouds to Direct Indoor Pedestrian Pathfinding

Balado

Díaz-Vilariño

Arias

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Increase in building complexity can cause difficulties orienting people, especially people with reduced mobility. This work presents a methodology to enable the direct use of indoor point clouds as navigable models for pathfinding. Input point cloud is classified in horizontal and vertical elements according to inclination of each point respect to n neighbour points. Points belonging to the main floor are detected by histogram application. Other floors at different heights and stairs are detected by analysing the proximity to the detected main floor. Then, point cloud regions classified as floor are rasterized to delimit navigable surface and occlusions are corrected by applying morphological operations assuming planarity and taking into account the existence of obstacles. Finally, point cloud of navigable floor is downsampled and structured in a grid. Remaining points are nodes to create navigable indoor graph. The methodology has been tested in two real case studies provided by the ISPRS benchmark on indoor modelling. A pathfinding algorithm is applied to generate routes and to verify the usability of generated graphs. Generated models and routes are coherent with selected motor skills because routes avoid obstacles and can cross areas of non-acquired data. The proposed methodology allows to use point clouds directly as navigation graphs, without an intermediate phase of generating parametric model of surfaces.</p>

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Section: Related Workmentioning

confidence: 99%

Point Clouds to Direct Indoor Pedestrian Pathfinding

Balado

Díaz-Vilariño

Arias

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Finally, specific space is normally needed to perform gait analysis using the above systems. In particular, the camera system may need more than 100 hundred square meters [ 10 , 11 , 12 , 13 ]. Table 2 lists a brief comparison of mainstream gait analysis methods.…”

Section: Related Workmentioning

confidence: 99%

MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

et al. 2018

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Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment.

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“…Moreover, MEMS magnetic, angular rate, and gravity (MARG) sensors are lightweight, low-cost, and more and more accurate, which greatly facilitates their application in indoor positioning [ 17 ]. Numerous studies, in terms of pedestrian dead reckoning (PDR) [ 18 , 19 , 20 ], intelligent robots [ 20 , 21 , 22 ], and indoor UAV navigation [ 23 ] are dedicated to estimating the attitude and heading based on MEMS MARG sensors. Currently, MARG sensors are embedded in each smartphone.…”

Section: Introductionmentioning

confidence: 99%

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Geng

Xia

2021

Micromachines

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The demands for indoor positioning in location-based services (LBS) and applications grow rapidly. It is beneficial for indoor positioning to combine attitude and heading information. Accurate attitude and heading estimation based on magnetic, angular rate, and gravity (MARG) sensors of micro-electro-mechanical systems (MEMS) has received increasing attention due to its high availability and independence. This paper proposes a quaternion-based adaptive cubature Kalman filter (ACKF) algorithm to estimate the attitude and heading based on smart phone-embedded MARG sensors. In this algorithm, the fading memory weighted method and the limited memory weighted method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. The latest step data is used as the memory window data of the limited memory weighted method. Moreover, for restraining the divergence, the filter innovation sequence is used to rectify the noise covariance measurements and system. Besides, an adaptive factor based on prediction residual construction is used to overcome the filter model error and the influence of abnormal disturbance. In the static test, compared with the Sage-Husa cubature Kalman filter (SHCKF), cubature Kalman filter (CKF), and extended Kalman filter (EKF), the mean absolute errors (MAE) of the heading pitch and roll calculated by the proposed algorithm decreased by 4–18%, 14–29%, and 61–77% respectively. In the dynamic test, compared with the above three filters, the MAE of the heading reduced by 1–8%, 2–18%, and 2–21%, and the mean of location errors decreased by 9–22%, 19–31%, and 32–54% respectively by using the proposed algorithm for three participants. Generally, the proposed algorithm can effectively improve the accuracy of heading. Moreover, it can also improve the accuracy of attitude under quasistatic conditions.

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Indoor Pedestrian Navigation Based on Conditional Random Field Algorithm

Cited by 7 publications

References 17 publications

Point Clouds to Direct Indoor Pedestrian Pathfinding

Point Clouds to Direct Indoor Pedestrian Pathfinding

MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

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