2.1. ZUPT/CUPT

In order to obtain a high accuracy navigation solution with a shoe-mounted IMU, ZUPT/CUPT can be applied to reduce the errors. They use the fact that when a pedestrian travels, one's feet alternate between a stance phase and a swing phase. If the stance phase appearing in each step can be detected correctly at a known (zero or constant) velocity, the INS velocity errors can be corrected periodically. More detailed explanation about ZUPT/CUPT can be found in Foxlin (Reference Foxlin2005) and Li et al. (Reference Li, Wang, Xiao and Luo2012).

It is important to correctly identify the stance phases of the IMU attached to a shoe and then apply ZUPT/CUPT to bind the navigation errors by providing proper EES (see Figure 1) with EKF or other estimation algorithms. There are two types of error in stance phase detection: misdetection and false-detection. It is noticed that a stance phase misdetection causes less serious problems for navigation accuracy than a false-detection. A misdetection just misses a chance for INS correction while a false-detection results in a wrong ‘correction’ and may cause the navigation results to become useless.

In order to get a high accuracy navigation solution, ZUPT/CUPT is applied with an EKF to reduce navigation errors. The framework of the proposed navigation system is illustrated in Figure 2. It has three main blocks: 1) INS Mechanization to compute the position, velocity and attitude; 2) Stance Phase Detection block to determine stance phases and apply ZUPT/ CUPT; 3) EKF block to estimate the error states and bound navigation errors.

Figure 2.

The main blocks in the framework used for pedestrian inertial navigation.

2.2. EKF

The EKF we used consists of 24 error states including navigation errors, sensor errors and gravity uncertainty. In each estimation cycle, once zero velocity has been detected, ZUPT delivers the updated error states to INS, the navigation error states are reset to zero after the current position, velocity and attitude is corrected. Other error states are accumulated and fed back to the inertial navigation mechanization.

The EKF includes the following 24 error states:

(1)$$\eqalign{ & \delta x_{Nav} = \left[ {\delta r_{N,} \delta r_{E,} \delta r_{D,} \delta v_{N,} \delta v_{E,} \delta v_{D,} \delta \varphi _{H,} \delta \varphi _{P,} \delta \varphi _R} \right]^T \cr & \delta x_{INS} = \left[ {\nabla _b, \nabla _f, \varepsilon _b, \varepsilon _f} \right] \cr & \delta x_{Grav} = \left[ {\delta g_{N,} \delta g_{E,} \delta g_D} \right]} $$

where δ x _Navδ x _INS and δ x _Grav are the navigation error vector, the IMU sensor measurement error vector and gravity uncertainty respectively. δ r δ v and δ φ are the position, velocity and attitude error vectors. ∇ expresses the accelerometer error vector, and ε is the gyro error vector. Subscript b stands for bias and subscript f stands for scale factor.

The Psi-angle INS error model is adopted in the system (Grejner-Brzezinska et al., Reference Grejner-Brzezinska, Toth and Yi2001):

(2)$$\eqalign{ & \delta \dot r = - w_{en} \times \delta r + \delta v \cr & \delta \dot v = - (w_{ie} + w_{in} ) \times \delta v - \delta \varphi \times f + \delta g + \nabla \cr & \delta \dot \varphi = - w_{in} \times \delta \varphi + \varepsilon} $$

where δr δv δφ are the position, velocity, and attitude error vectors respectively, ∇ is the accelerometer error vector, δg is the error in the computed gravity vector, ε is the gyro error vector, f is the specific force vector, w _ie is the earth rotation rate vector, w _en is the transport rate vector and w _in is the angular rate vector from the navigation to the inertial frame. The dynamic matrix is obtained by a linearization of Equation (2).

(3)$$\left[ {\matrix{ {\delta \dot x_{Nav}^{1 \times 9}} \cr {\delta \dot x_{grav}^{1 \times 3}} \cr {\delta \dot x_{IMU}^{1 \times 12}} \cr}} \right] = \left[ {\matrix{ {F_{11}^{9 \times 9}} & {F_{12}^{3 \times 9}} & {F_{13}^{12 \times 9}} \cr {0^{9 \times 3}} & {F_{22}^{3 \times 3}} & {0^{12 \times 3}} \cr {0^{9 \times 12}} & {0^{3 \times 12}} & {0^{12 \times 12}} \cr}} \right] = \left[ \matrix{\delta x_{Nav} \cr \delta x_{grav} \cr \delta x_{IMU} } \right] + \left[ \matrix{w_{Nav} \cr w_{grav} \cr w_{IMU} } \right]$$

where w _Navw_IMU and w _grav are all zero-mean Gaussian white noise vectors. A detailed expression of the dynamic matrix can be found in Grejner-Brzezinska et al. (Reference Grejner-Brzezinska, Toth and Yi2001).

For ZUPT/CUPT, the observation z ^1×3 is the velocity vector in three directions, and the measurement model is:

(4)$$z = H\delta x + {\rm n}$$

where H^3×24 = [O ^3×3I ^3×3O ^3×3] and n is the measurement noise vector.

Consider the error state ${\rm \;} \delta x = \; \hat x - x$ where $\hat x$ is the estimated state and x is the true state, the observation z is $\hat x - 0$ for ZUPT and $\hat x - v_c $ or CUPT, where $\hat x$ is computed by the strap-down inertial navigation mechanization and v _c is the constant velocity.

In addition to pursuing reliable observation to improve an EKF's estimation, as we are doing here, some other approaches are also conducted, such as the quantization and coloured noises error modelling for inertial sensors (Han and Wang, Reference Han and Wang2011), and adaptive EKF (Wang et al., Reference Wang, Ding and Wang2007). This paper's scope only covers the approaches for obtaining reliable observation with ZUPT/CUPT.

2.3. Stance Phase Detection

The stance phase detector we proposed consists of the following four logical conditions C₁-C₄, using both the accelerometer and gyro measurements. These four conditions must be satisfied (=1) simultaneously to declare a foot as stationary for every sample epoch k.

1. 1) The magnitude of the acceleration |a _k| where a _xka_yk a_zk are the magnitude of the acceleration on x, y, z-axis respectively:

   (5a)$$\eqalign{& |a_k | = [a_{xk}^2 + a_{yk}^2 + a_{zk}^2 ]^{1/2} \cr & C1 = \left\{ {\matrix{ 1 & {threshold_{amin} \lt |a_k | \lt threshold_{amax}} \cr 0 & {otherwise} \cr}} \right.} $$

2. 2) The magnitude of the acceleration on z-axis |a _zk|:

   (5b)$$C2 = \left\{ {\matrix{ 1 & {threshold_{azmin} \lt \left| {a_{zk}} \right| \lt threshold_{azmax}} \cr 0 & {otherwise} \cr}} \right.{\rm \; \; \;} $$

3. 3) The magnitude of the angular velocity |w _k|:

   (5c)$$\eqalign{& |w_k | = [w_{xk}^2 + w_{yk}^2 + w_{zk}^2 ]^{1/2} \cr & C3 = \left\{ {\matrix{ 1 & {|w_{yk} | \lt threshold_{wmax}} \cr 0 & {otherwise} \cr}} \right.{\rm \; \; \;}} $$

4. 4) The magnitude of the angular velocity on y-axis |w _yk|:

   (5d)$$C4 = \left\{ {\matrix{ 1 & {|w_{yk} | \lt threshold_{ywmax}} \cr 0 & {otherwise} \cr}} \right.{\rm \; \; \;} $$

Since a foot firmly contacts the ground in a stance phase, the acceleration and rotation of the foot is zero over this duration. In an ideal condition the magnitude of total angular velocity |w _k| in Equation (5c) and acceleration in a horizontal plane should be zero during a stance phase. However, the actual measurements are not zero due to vibration and the sensors' bias, but are still lower than the given thresholds.

By observing the outputs of a shoe-mounted IMU, it is found that the z-axis acceleration a _zk and the y-axis angular velocity w _yk are the most significant indicators in pedestrian travel. Due to the tilt of a shoe's surface, however, the measured |w _yk| and |a _zk| are not exactly 0 and g respectively. So the thresholds in Equations (5b) and (5d) are set based on the average value of z-axis accelerometer and y-axis gyro outputs during an initial stationary period, plus a certain level of fluctuation to ensure its robustness. The initial stationary period is at the beginning of sensor data collection when the shoe-mounted IMU is in a stationary condition.

2.4. Height Constraint

Similar to the concept proposed by Abdulrahim et al. (Reference Abdulrahim, Hide, Moore and Hill2012), we assume that the changes in height are only caused by climbing stairs since the floor is level in most indoor environments. If a change in height over one step is above a threshold, we regard it as reaching a new level or a stair; otherwise the height will be maintained at the previous level and uses this as a measurement of the Kalman filter. This constraint works well in structured environments with only level floor and stairs, but fails in the case of gently sloping terrains.

3.1. Footstep Detector

In pedestrian navigation with a shoe-mounted IMU, especially for running, it is important to know when a pedestrian takes a step. A footstep detector can not only mark the beginning of a gait cycle, but also helps to segment the pedestrian's gait cycle into four phases: stance, heel-off, swing, heel strike. A footstep detector mainly detects the heel strike after a foot completes swing phase in the air, reaches the highest point and touches down on the ground. Once the heel has hit the ground, the deceleration of the forefoot is very dramatic and characterised by large changes in the acceleration profile. Thus, we simplify the characteristic thus: in heel strike phase, the z-axis acceleration experiences a monotonic decreasing that is unique and obvious to detect so we denote it as a new step.

3.2. Swing Phase Detector

A swing phase detector is designed to identify swing phases and prevent false stance phase detection in swing phases when enlarging the thresholds of the stance phase detector for the running case. The swing phase detector introduced here is based on the energy of rotation threshold T _S. $y_k^g $ is the angular velocity at the time instant k, W is the window size. The swing phase detector is combined with the footstep detector to separate a swing phase from other phases. As swing phases are identified, false stance phase detection in swing phases can be avoided.

(6)$$T(k) = \textstyle{1 \over W}\sum \limits_{k}^{k + W - 1}}\hskip-2pt \parallel y_k^g\hskip-2pt \parallel ^2 \lt T_s $$

Figure 3 shows the energy of rotation T in two gait cycles. It should be noted that the footstep detector can also use this T to detect heel strikes. The energy of rotation goes through a big jump when the foot falls down to the ground, as Figure 3 indicates.

Figure 3.

The energy of rotation T.

4.1. Elevator

Taking an elevator as example, Figure 4 shows the measurement of the vertical accelerometer Acc_Z and the estimated velocity. A pedestrian walked into the elevator and stood still when the elevator was moving. As indicated by the velocity in Figure 4, the elevator went through a period of acceleration, kept at constant velocity and decelerated before it was stationary again. The constant velocity appears once the elevator finishes accelerating and before the elevator starts slowing down. In this circumstance, the most significant indicator is the acceleration in the vertical direction.

Figure 4.

Indication of motion in an elevator.

When the elevator starts to move, the acceleration in the z-axis experiences a jump which can be easily distinguished from the normal gaits. When this special case of acceleration has been detected, the velocity is calculated by the inertial navigation algorithm without a velocity measurement update. Since the pedestrian stands in the elevator, the zero horizontal velocity can be added as a measurement to the EKF, referencing to Equation (4):

(7)$$z = \hat x - v_{XY} $$

After the elevator finishes accelerating, the velocity keeps constant before the elevator begins to slow down. During this constant velocity section, CUPT is applied to correct navigation errors. So the measurement z is $\hat x - v_{constant} $. The decelerating section is similar to the accelerating section, and the zero horizontal velocity update is applied.

During the accelerating and decelerating sections, elevator data processing follows the INS mechanization to obtain position, velocity and attitude information without any velocity update until a constant velocity is detected. However, in some cases it is hard to detect constant velocity. The motion in accelerating and decelerating sections actually has a variable acceleration rate. Especially during braking, an elevator first slows down to a low speed, then keeps at this low constant velocity for a while, before decelerating to zero velocity. As a result, frequent elevator stops have an influence on the accuracy of height measurement.

4.2. Escalator

For the case of being on an escalator, the horizontal velocity is the most significant indicator. When a foot steps onto the escalator and keeps pace with the escalator going up or down, a constant velocity is determined by the instance of the foot being set on the escalator. So in this case, the measurement z is $\hat x - v_{constant} $. Once the horizontal velocity is over a threshold in stance phase, it is assumed that the foot is on an escalator and we apply CUPT as measurement of the EKF.

Figure 5 shows the horizontal and vertical velocity of a foot on a moving escalator. We can see that ZUPT is applied when the pedestrian walks on the ground; the observation is zero velocity during the stance phase, while the velocity keeps constant when the pedestrian stands on the escalator and CUPT corrects the velocity error using the constant velocity as the observation.

Figure 5.

Motion in an escalator with CUPT.

In summary, the stance phase detection for CUPT is the same with ZUPT since the foot is statically in contact with a platform in both cases. But for CUPT the velocity of the moving platform needs to be measured. ZUPT is applied when a person is walking on the ground while CUPT is to handle the case on a moving platform. In order to apply CUPT, firstly we need to use the stance phase detector to declare whether the foot is stationary, then determine whether the foot is on a moving platform, measure the constant velocity of the platform and apply CUPT to correct the errors.

5.1. RTS Smoother

The RTS smoother is a two-pass smoothing algorithm. The forward pass is the same as the regular Kalman filter algorithm. During the prediction and update steps, the states and covariance are saved for use in the backwards pass. After the forward pass, the RTS smoothing begins from the end of the mission and moves back to the starting point of the data set. In the backwards pass, the smoothed state estimates and covariance propagate using the states saved in the forward pass (Rauch et al., Reference Rauch, Striebel and Tung1965). The smoothed estimate of the states is computed using the equations below.

(8)$$\hat x_{k|N} = \hat x_{k|k} + C_k [(\hat x_{k + 1|N} - \hat x_{k + 1|k} ]$$

(9)$$P_{k|N} = P_{k|k} + C_k (P_{k + 1|N} - P_{k + 1|k} )C_k {\rm '}$$

where $C_k = P_{k|k} \O ^{'\left( {k + 1,k} \right)P_{k + 1|k} ^{ - 1}}, \; \hat x_{k|k}, \; \hat x_{k + 1|k}, \; P_{k|k}, \; P_{k + 1|k} $ are the error states and their variances stored in the forward filter.

5.2. Data Segmentation

The implementation of the RTS smoother has some limitations. Firstly, it is commonly applied for post processing. Secondly, a lot of memory is required to store the predicted and updated information (Chiang et al., Reference Chiang, Duong, Liao, Lai, Chang, Cai and Huang2012). In order to achieve near-real-time smoothing and reduce the storage burden, one intuitive way is to take profit of the detected zero velocity intervals and divide the step with them.

In order to get near-real-time smoothing, proper segmentation rules should be applied. The principle for our data segmentation is similar to the one proposed by Colomar et al. (Reference Colomar, Nilsson and Handel2012). They use velocity error covariance as a threshold. The choice of the threshold value is based on typical values from the training data of random walking, so it is not suitable for scenarios that involve running. Moreover, it is not applicable for navigation on a moving platform, such as elevator or escalator, where the velocity of a foot in stance phase may not converge to zero but a constant velocity. In addition, the fixed time constant of 30 samples that they applied may work properly in normal walking but fail occasionally in running, since the duration of stance phase in running may be shorter than 30 samples and it will cause misdetection. Furthermore, if the person alternates between walking and running, the segmentation rule based on velocity error covariance will easily get confused and become invalid.

We proposed a new segmentation rule by combining both the footstep detector and swing phase detector. The energy of rotation threshold T is used to avoid false segmentation in swing phases, which makes the rule more flexible to meet the complex environment.

The segmentation point should be in the stance phase. Here we have tried three methods to get the segmentation point; the first one is to use the middle point of the stance phase, the second one is the sample point with smallest T during the stance phase and the third one is the sample point with T under a certain small threshold. According to our tests, these three methods have little difference in the final smoothing results, which means that we do not need to find where the exact point converges to zero, as long as the segmentation point is within the stance phase. The third method is used as the segmentation rule in the following closed-loop smoothing.

5.3. Closed-Loop Smoothing

In our ZUPT-aided INS implementation, the predicted EKF error states are always reset to zero. During measurement updates, the estimated error states are corrected and fed back to compensate the estimated navigation states. However, in a closed-loop smoothing-aided ZUPT, the EKF runs as normal but the predicted error states need to be propagated and no longer reset to zero. The estimated states and covariance are updated only when ZUPT are applied and the smoothing is applied over the error states. After the step segmentation is performed, the estimated states are combined with the smoothed error states to get the final navigation results. Figure 6 summarises the implementation procedures for the closed-loop smoothing.

Figure 6.

Step wise close loop smoothed ZUPT aided INS.

6.1. Hardware Description

We implemented our pedestrian navigation system with the Navchip IMU from Intersense. The small size of Navchip makes it easy to attach to a boot and has no effect on walking. Table 1 lists the specification of Navchip (encapsulated within casing).

Table 1.

Specification of Navchip IMU.

6.2. Walking and Running Test

6.2.1. Experimental data processed results

All the trajectories are closed-loop with the finish point the same as the start point. Since the start and stop points are the exact same location in reality, the distance between the black and blue dots in the below figures represents the error on the horizontal plane. Figure 7 represents three trajectories including stair climbing.

Figure 7

(a) 3D trajectory 1, (b) 3D for trajectory 2 and (c) 3D trajectory 3.

Figure 8

(a) Trajectory 1 and (b) Trajectory 2.

Table 2 shows the final return position errors for three-dimensional (3D) experiments.

Table 2.

Return Position Errors.

In this section we present two trajectories (Figure 8) and each trajectory is traversed several times with different human gaits that include walking, running and alternating between walking and running. All the trajectories are closed-loop.

Table 3 summarises the final return position errors for each trajectory and Table 4 also gives the comparison results of different gait styles.

Table 3.

Return Position Errors.

Table 4.

Errors of Different Gait styles.

Ideally the error of running should be larger than that of alternating between walking and running, since the higher dynamics experienced during running than in walking result in fewer ZUPT applications. However, from our results as shown in Table 4, we get a different conclusion. The main reason is that when the pedestrian changes his gait randomly, sometimes it is easy to confuse the footstep detector. On the other hand, the swing phase detector is applied to ensure that no false stance phase detection in swing phases, thus we can still get reasonable results.

In addition, out of the excessive shock of feet strikes, the bi-directional overflows exceed the ranges of accelerometer and/or gyroscope and will affect navigation accuracy to a large extent. Although we have already taken measures to reduce the impact, it is still inevitable that the overflow would affect the INS mechanism computing vertical velocity and position.

6.3. Elevator and Escalator Test

This section describes elevator and escalator tests conducted in the UTS tower building.

6.3.1. Elevator test

The pedestrian walked for a while before going into an elevator. The pedestrian randomly chose which elevator to take and which level to stop at. When the door was closed, the pedestrian stopped walking and stayed still until the door opened again.

Three trajectories are shown in Figure 9. The left figure is the 3D view of the trajectory while the right figure represents the height change over time shown by the red line. The black dots in the right figures indicate the sample points have been applied CUPT in the slope sections with a constant velocity, or ZUPT in the flat sections. It is noticed that only a few small sections of the red line in the right figures are not covered by the black dots, which are the periods that the elevator is accelerating or decelerating where neither ZUPT nor CUPT can be applied for vertical position and speed correction.

Figure 9

(a) Trajectory 1 of elevator test, (b) Trajectory 2 of elevator test and (c) Trajectory 3 of elevator test.

6.3.2. Escalator test

Tests were also conducted for an escalator. As shown in Figure 10, starting at a lower level, the pedestrian walked for a while and then stood on a rising escalator with the IMU mounted on one foot. After getting off the escalator at the middle level the pedestrian walked to another rising escalator to the top level; and then walked to the two falling escalators in sequence to descend back to the start point.

Figure 10.

Trajectory of escalator test.

Table 5.

Distance-Travelled Errors Normalized to the Total Distance Travelled.

Figure 10 is the 3D view of the trajectory, which clearly represents the four escalators and the three levels walked. Tables 6 and 7 summarise the return position errors for elevator and escalator experiments respectively.

Table 6.

Return Position Errors for Elevator.

Table 7.

Return Position Errors for Escalator.

Although these test results are reasonably good, there is still plenty of room to further improve the proposed algorithm to achieve better performance and handle more challenging situations. For the escalator, the vertical angle of the escalators was predetermined to compute the vertical velocity. This angle could be measured by the sensor with more sophisticated investigation. Although the height relative error in Table 7 is small, the horizontal relative error is not so good. This may be due to the change in the vertical angle at the two end sections of escalators where the staircase keeps moving forward but does not change in the vertical direction. The predetermined slope angle introduces error for the measurement update in the short end sections.

6.4. Smoothing Results

All experimental tests have been conducted in UTS, including walking, running and taking escalator and elevator. The figures below show the achieved smoothing effect compared with the estimated trajectory.

Figures 11 and 12 show the effect of smoothing over a walking and running trajectory respectively. From the height and velocity analysis, we can see that when the first ZUPT is applied, the correction applied over the estimated trajectory results in a big jump appearing in every step. After smoothing, these big jumps can be eliminated thus the trajectory is smoother than the original one.

Figure 11.

Effect of smoothing on a walking trajectory.

Figure 12.

Effect of smoothing on a running trajectory.

Figure 13.

Effect of smoothing on an elevator trajectory.

Figure 14.

Effect of smoothing on an escalator trajectory.

Compared with walking, the magnitude of running velocity may vary and not converge to exactly zero as walking does during the stance phase.

For the cases in an elevator and on an escalator, CUPT is applied to let velocity not converge to zero as ZUPT, but to a constant velocity when the pedestrian stands on the moving platform. It is also obvious to see that the smoothing algorithm can significantly improve the smoothness of the trajectory especially when the elevator finishes accelerating or decelerating and the pedestrian gets on or off the escalator (Figures 13 and 14).