An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation

Abstract

Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions.

3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

“…Their common setup is to attach a single camera and IMU sensor together on a fixed platform [12,31,44,45]. In order to integrate the Inertial System (INS) and video, common methods include Particle Filter (PF) [2,46], Kalman Filters (KF) [13,47] and its extensions such as Extended Kalman Filters (EKF) [7,36] and Unscented Kalman Filters (UKF) [31].…”

“…However, it has a huge potential due to its relatively higher accuracy with increasing availability in the form of surveillance videos [1,35]. The reason of not having a significant amount of work on OPS could be explained by its performance vulnerability with the possibility of obstruction of the Line-of-Sight (LoS) signals (between the camera and the targets), which is common inside the buildings, and generally indoors [36,37]. This may result in failure of the OPS to provide a reliable and continuous positioning solution.…”

Indoor Pedestrian Dead Reckoning Calibration by Visual Tracking and Map Information

“…They can provide relative user positions, orientation and velocity in indoor area by using triad accelerometers and gyroscopes for step detection and heading estimation [20][21][22][23][24][25]. PDR-based methods can be divided into several categories depended on the implementation of IMU: foot-mounted [26,27], hand-held [17,28], backpack [29][30][31], in-pocket [32] or head-mounted [27,33,34], and this study mainly focuses on hand-held ones on smartphones. This is because that smartphones have been integrated into ordinary and spaces of daily life [6], and Location-Based-Services (LBS) such as navigation and tracking, has been widely used by people around the world [5,6].…”

A Low-Cost, Wearable Opto-Inertial 6-DOF Hand Pose Tracking System for VR

“…Optical systems do not require mapping and provide relatively high accuracies over a large workspace. However, a constant line of sight between the IR LEDs and the camera must be maintained [10]. The performance of this optical system can be affected by occlusion and limiting the coverage area.…”