Human Gait Estimation Using Multiple 2D LiDARs

Abstract

This paper proposes a human gait estimation algorithm using a multiple 360 degree 2D LiDARs system. The system is fixed on the ground at the shin height to scan human legs during gait. The multiple LiDARs system is to overcome the drawbacks of a single LiDAR system, which could lose data due to occlusion between legs during walking and has a short tracking range. The performance of a sensor fusion system strongly depends on the calibration. In this paper, we propose a calibration method using a cylinder with known radius as a specific marker. In contrast to other methods, the calibration parameters and the cylinder center points at different positions are estimated by a proposed iterative algorithm. The measurement noises in the LiDAR output are considered to increase the accuracy of calibration and human leg center points estimation. Instead of using least square fitting of circle algorithm to estimate the leg center point, a new iterative algorithm which includes measurement noises is proposed. Although multiple LiDARs are used, the discontinuities of leg center points could still happen. Therefore, a quadratic optimization based eighth-order splines algorithm is derived to interpolate and smooth the data. Two configurations of three LiDARs are tested in the experiment. The former is the triangle configuration in which the whole walking path is covered by all three LiDARs. This configuration minimizes the occlusion between legs. The maximum RMSE of step length estimation of this configuration compared with the optical camera system is 0.03m. The latter is the line configuration in which each LiDAR covers a certain walking path sequentially. This configuration maximizes the tracking range. The experiment with 20m straight walking has the RMSE of about 0.10m.

A Human Gait Tracking System Using Dual Foot-Mounted IMU and Multiple 2D LiDARs

“…The LiDAR scan data are then used to estimate the human leg center point trajectories using the algorithm in [27,31]. The outputs of this module are the human leg positions in stance phases which are represented by diamond shapes in Figure 3.…”

“…The covariance of LiDAR measurements can be found in [31]. Then, we can compute the covariance matrix of measurement noises of (8) as follows:…”

“…where Φ k is defined in (23) and (31) is the numerical integration of quaternion, position, and velocity from kτ to (k + 1)τ. Using ( 28), (30) and (31), we can expand the left side of (29) as the errors of the numerical integration of the Kalman filter estimated values. Thus, (29) represents how the estimation errors evolve after the integration of (31).…”

“…Using ( 28), (30) and (31), we can expand the left side of (29) as the errors of the numerical integration of the Kalman filter estimated values. Thus, (29) represents how the estimation errors evolve after the integration of (31).…”

“…However, the effective human leg tracking range of a single 2D LiDAR is limited within a few meters. In [31], a fusion system of multiple 2D LiDARs has been proposed to extend the human leg tracking range. However, the LiDARs cannot be placed far apart to guarantee the continuity of leg scan data.…”