We acquire data sets with the ground truth VICON pose in the experimental room. We evaluate the proposed algorithm with the 360 ◦ rotational motion data set, i.e. the sequence named turn around, and also evaluate the algorithm with the high speed rotational motion data set called the sequence high speed. The grey images and mapping results for turn around are shown in Fig. 5.6 and those high speed are shown in Fig. 5.8. We use the root mean square (RMS) error of rotational position as the evaluation metric, with VICON data for ground truth. We compare the proposed algorithm with the other method such as IMU, integrated angular velocity from [1] for each sequence. The overall RMS error tables are shown in Table 5.1 and Table 5.2.
In the sequenceturn around, the event camera rotates 360◦ with respect to the Y-axis. There are many events in the direction perpendicular to the Y-axis, resulting in much smaller drift error on the Y axis than on the other axes in Table 5.1. In contrast, IMU accumulated more drift error on the Y-axis and less drift error in the other axes.
The grey images of the sequence are severely blurred as shown in Fig. 5.8a, which could not be processed by the conventional vision algorithms. In the sequencehigh speed, the proposed method estimates rotational position stably despite the high speed ego motion, while angular velocity is not properly integrated to estimate the rotational position.
(a) The room (b) The lobby (c) Outside of the building
Figure 5.2: The grey image of VICON-free data sets.
(a) The result from [1] (b) The proposed method
Figure 5.3: The projected map from [1] and the proposed method in the room.
(a) The result from [1] (b) The proposed method
Figure 5.4: The projected map from [1] and the proposed method in the lobby.
(a) The result from [1] (b) The proposed method
Figure 5.5: The projected map from [1] and the proposed method outside of the building.
(a) The grey images (b) The brightened grey images (c) The projected map images
Figure 5.6: The grey image and the projected map with the proposed method in sequence, turn around.
(a)
‘
(b)
Figure 5.7: The rotational position estimation result of turn around. (a) rotational position and (b) rota-
(a) The grey images (b) The projected map images
Figure 5.8: The grey image and the projected map with the proposed method in sequence,high speed.
(a)
(b)
Figure 5.9: The rotational position estimation result ofhigh speed. (a) rotational position and (b) rotational
6
Conclusion
This paper presented the rotational motion estimation method using only an event camera. We used the 3D spherical map in order to reliably estimates rotational angles in all angular regions without a singularity or the gimbal lock problem. We evaluated the proposed algorithm with vari- ous data sets including the high speed ego motion sequence and the high dynamic range sequences.
The proposed method gives more accurate results than the integral of angular velocity in angular position estimation, and shows even higher accuracy than the IMU, within the maximum error of 2 degrees. In conclusion, the proposed method maximizes the advantage of the event camera on estimating rotational motion. The algorithm can be fully applied to the fast angular motion estimation for AR/VR applications where pure rotational motion occurs frequently. As a future work, we will conduct the 6-DOF motion estimation using both event cameras and grey cameras, which also takes the advantage of the properties of event cameras.
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