An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review

Integrated Ultra-wideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensor systems have been gaining popularity for pedestrian tracking and indoor localization applications, mainly due to their complementary error characteristics that can be exploited to achieve higher accuracies via a data fusion approach. These integrated sensor systems have the potential for improving the ambulatory 3D analysis of human movement (estimating 3D kinematics of body segments and joints) over systems using only on-body MIMUs. For this, high accuracy is required in the estimation of the relative positions of all on-body integrated UWB/MIMU sensor modules. So far, these integrated UWB/MIMU sensors have not been reported to have been applied for full-body ambulatory 3D analysis of human movement. Also, no review articles have been found that have analyzed and summarized the methods integrating UWB and MIMU sensors for on-body applications. Therefore, a comprehensive analysis of this technology is essential to identify its potential for application in 3D analysis of human movement. This article thus aims to provide such a comprehensive analysis through a structured technical review of the methods integrating UWB and MIMU sensors for accurate position estimation in the context of the application for 3D analysis of human movement. The methods used for integration are all summarized along with the accuracies that are reported in the reviewed articles. In addition, the gaps that are required to be addressed for making this system applicable for the 3D analysis of human movement are discussed.

Adaptive Multi-Pedestrian Tracking by Multi-Sensor: Track-to-Track Fusion Using Monocular 3D Detection and MMW Radar

Accurate and reliable tracking of multi-pedestrian is of great importance for autonomous driving, human-robot interaction and video surveillance. Since different scenarios have different best-performing sensors, sensor fusion perception plans are believed to have complementary modalities and be capable of handling situations which are challenging for single sensor. In this paper, we propose a novel track-to-track fusion strategy for multi-pedestrian tracking by using a millimeter-wave (MMW) radar and a monocular camera. Pedestrians are firstly tracked by each sensor according to the sensor characteristic. Specifically, the 3D monocular pedestrian detections are obtained by a convolutional neural network (CNN). The trajectory is formed by the tracking-by-detection approach, combined with Bayesian estimation. The measurement noise of the 3D monocular detection is modeled by a detection uncertainty value obtained from the same CNN, as an approach to estimate the pedestrian state more accurately. The MMW radar utilizes the track-before-detection method due to the sparseness of the radar features. Afterwards, the pedestrian trajectories are obtained by the proposed track-to-track fusion strategy, which can work adaptively under challenging weather conditions, low-illumination conditions and clutter scenarios. A group of tests are carried out to validate our pedestrian tracking strategy. Tracking trajectories and optimal sub-pattern assignment (OSPA) metric demonstrate the accuracy and robustness of the proposed multi-sensor multi-pedestrian tracking system.