Accurate human motion capture in large areas by combining IMU- and laser-based people tracking

Abstract

Abstract-A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial sensors that allow for free and large-scale motion but suffer from drift in the pose estimate. This paper presents a novel tracking approach that aims to provide globally aligned full body posture estimates by combining a mobile robot and an inertial motion capture system. In our approach, a mobile robot equipped with a laser scanner is used to anchor the pose estimates of a person given a map of the environment. It uses a particle filter to globally localize a person wearing a motion capture suit and to robustly track the person's position. To obtain a smooth and globally aligned trajectory of the person, we solve a least squares optimization problem formulated from the motion capture suite and tracking data. Our approach has been implemented on a real robot and exhaustively tested. As the experimental evaluation shows, our system is able to provide locally precise and globally aligned estimates of the person's full body posture.

Experimental Comparison of Sensor Fusion Algorithms for Attitude Estimation

“…Inertial Measurement Unit (IMU) sensors are a technology capable of estimating orientation of a rigid body so they are largely used as an implementation of real-time motion capture systems to track the location and the body posture of people (see Ziegler et al (2011), Prayudi and Doik (2012)) in contrast to optical solutions such as (Falco et al, 2012), or to measure the joint angles for gait analysis for rehabilitation purposes and biomedical applications as well as for performance assessment of the aging population (see Zecca et al (2013) and Bennett et al (2013). Also, because of the small size and low weight that make it better suited to the purpose, the interest for IMU-based systems is growing in advanced robotic applications, i.e., localization and wheel slip estimation of a skid steered mobile robot (Jingang et al (2007)), position and attitude determination for unmanned airborne vehicles (UAVs) (Joong-hee et al (2011)) and unmanned underwater vehicles (UUVs) (Kim et al (2011)).…”

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

“…In the data ( Section 3.1 ), the point clouds corresponding to the lower leg ( ) are clustered with the aid of the joint positions and bone segment lengths. Our approach employs a similar technique to detect the leg position in the point clouds, as proposed by [ 34 ]. In the current work, we used a particle filter [ 39 ] to track the lower leg-bone point cloud.…”

“…However, IMU-based pose tracking is not mature enough to detect accurate positional data for individual joints [ 29 ] and is majorly used for motion analysis in rehabilitation and physiotherapy [ 30 , 31 ]. To counter this, a merger of IMU data with depth cameras has been attempted [ 32 , 33 , 34 ]. In [ 32 ], the fusion of sensors is adapted to validate the acquired movement data in two steps (generative and discriminative).…”

“…In the lidar-IMU fusion experiment, the IMU sensor provides orientation information, whereas the lidar is used to filter the data. A similar approach was proposed by [ 34 ], where the IMU sensor detects human rotation and a laser sensor detects the human body position to correct the drift over time. The approach presents only the trajectory of the human motion in outdoor environment but the full skeleton pose is not described.…”

Motion2Vector

“…Significant work in HAR focuses on the use of inertial sensor to detect human activities [10,16,18]. This is motivated primarily by the wide availability of such sensors on consumer devices such as smartphones and smart watches.…”