An Open-Source Platform for Human Pose Estimation and Tracking Using a Heterogeneous Multi-Sensor System.

Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detection and estimation of height, skeletal parameters, position, and orientation by fusing lidar and inertial sensor data. Finally, the estimated data are reconstructed on a virtual 3D avatar. The proposed human pose tracking system was developed using open-source platform APIs. Experimental results verified the proposed human position tracking accuracy in real-time and were in good agreement with current multi-sensor systems.

Fusion of Multiple Lidars and Inertial Sensors for the Real-Time Pose Tracking of Human Motion.

Today, enhancement in sensing technology enables the use of multiple sensors to track human motion/activity precisely. Tracking human motion has various applications, such as fitness training, healthcare, rehabilitation, human-computer interaction, virtual reality, and activity recognition. Therefore, the fusion of multiple sensors creates new opportunities to develop and improve an existing system. This paper proposes a pose-tracking system by fusing multiple three-dimensional (3D) light detection and ranging (lidar) and inertial measurement unit (IMU) sensors. The initial step estimates the human skeletal parameters proportional to the target user's height by extracting the point cloud from lidars. Next, IMUs are used to capture the orientation of each skeleton segment and estimate the respective joint positions. In the final stage, the displacement drift in the position is corrected by fusing the data from both sensors in real time. The installation setup is relatively effortless, flexible for sensor locations, and delivers results comparable to the state-of-the-art pose-tracking system. We evaluated the proposed system regarding its accuracy in the user's height estimation, full-body joint position estimation, and reconstruction of the 3D avatar. We used a publicly available dataset for the experimental evaluation wherever possible. The results reveal that the accuracy of height and the position estimation is well within an acceptable range of ±3-5 cm. The reconstruction of the motion based on the publicly available dataset and our data is precise and realistic.