A single foot-mounted pedestrian navigation algorithm based on the maximum gait displacement constraint in three-dimensional space

Abstract

Inertial navigation technology composed of inertial sensors is widely used in foot-mounted pedestrian positioning. However, inertial sensors are susceptible to noise, which affects the performance of the system. The zero-velocity update (ZUPT) as a traditional method is utilized to suppress the cumulative error. Unfortunately, the walking distance calculated by a Kalman filter still has position error. To improve the positioning accuracy, a nonlinear Kalman filter with spatial distance inequality constraint for single foot is proposed in this work. Since the stride distance between adjacent stance phases has an upper bound in plane and height, an inertial navigation system (INS) established by one inertial measurement unit (IMU) is adopted to constrain the stride process. Eventually, the performance of the proposed method is verified by experiments. Compared to the single foot-mounted ZUPT method, the proposed method suppresses the plane error and the height error by 46.04% and 65.48%, respectively. For the dual foot constraint method, the proposed constraint method can reduce the number of sensors while ensuring the positioning accuracy. Moreover, the height error is reduced by 59.98% on average by optimizing the constraint algorithm. The experimental results show that the trajectory estimated by the proposed method is closer to the actual path.

Neural inertial navigation system on pedestrian

“…Because low-cost MEMS IMUs are plagued by high sensor noise and bias, error accumulates rapidly during double integration. A commonly used solution is ZUPT [8][9][10], where IMU is mounted on a pedestrian's foot. By using the constraint of zero velocity when the foot touches the ground, Kalman filtering is utilized to compensate for error.…”

“…Therefore, it is necessary to tackle the high sensor noise for a robust and precise inertial navigation system. To compensate for the error and improve localization accuracy without the aid of other sensors, traditional methods rely on human motion laws and special constraints to reduce the cumulative error, such as Zero velocity UPdaTe (ZUPT) [8][9][10], Zero Angular Rate Update (ZARU) [11] and step-based pedestrian dead reckoning methods (PDR) [12,13]. However, these methods demand IMU is fixed to a special position for a pedestrian, such as a foot, and are only suitable for periodic pedestrian motion.…”

Research on Navigation and Positioning of Electric Inspection Robot Based on Improved CNN Algorithm

“…The convolutional layer is a key component of the HM-DCNN, and its primary function is to calculate the multiplication of the receiving field point and the rotation of the filter (or core) that can be studied. After the conversion operation, a nonlinear sample is performed in the aggregation layer to reduce the dimensionality of the data [22,23]. The most common integration strategy is the most consolidated and average consolidation.…”

Anatomical mining method of cervical nerve root syndrome under visual sensing technology

“…The anatomical image of cervical nerve root syndrome is based on the difference of pixel gray, and the image gray value is vulnerable to noise [12]. The neighborhood information of the pixels of the anatomical image of cervical nerve root syndrome can prevent the image from being affected by noise.…”

“…The direction from the center of the feature point in the image block to the image centroid is the direction of the feature point in the anatomical image of cervical nerve root syndrome. The direction expression of feature points in the anatomical image of cervical nerve root syndrome is as follows: (12) The directional binary simple descriptor method is sensitive to noise. The directional binary simple descriptor method uses the pixel average value to represent the gray value of a point, which can effectively avoid noise interference [16].…”