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In this paper, heading reference-assisted pose estimation (HRPE) has been proposed to compensate inherent drift of visual odometry (VO) on ground vehicles, where an estimation error is prone to grow while the vehicle is making turns or in environments with poor features. By introducing a particular orientation as “heading reference,” a pose estimation framework has been presented to incorporate measurements from heading reference sensors into VO. A graph formulation is then proposed to represent the pose estimation problem under the commonly used graph optimization model. Simulations and experiments on KITTI data set and our self-collected sequences have been conducted to verify the accuracy and robustness of the proposed scheme. KITTI sequences and manually generated heading measurement with Gaussian noises are used in simulation, where rotational drift error is observed to be bounded. Compared with a pure VO, the proposed approach greatly reduces average translational localization error from 153.85 to 24.29 m and 23.80 m in self-collected stereo visual sequences with traveling distance over 4.5 km at the processing rates of 19.7 and 11.1 Hz, for the loosely coupled and tightly coupled models, respectively. Note to Practitioners—When the Global Positioning System is not available or reliable, visual odometry (VO) on ground vehicles is an efficient tool for estimating the pose, which involves translation and rotation. However, VO inherently suffers from drifting issue due to constant iterations. By adding a low-cost heading reference sensor, this paper first introduces graph optimization formulation of pose estimation and then presents a pose estimation framework which incorporates heading measurements to VO such that long-term translation and rotation estimation errors can be greatly reduced in real-time computation. As a supplementary to VO, performance may still deteriorate in environments with poor illumination conditions and high-frequency movements. The proposed approach may be further improved by fusing heading measurements from more sensors or being used to build a heading reference-assisted simultaneous localization and mapping (SLAM) system on any off-the-shelf SLAM framework. [ABSTRACT FROM AUTHOR]

Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. [ABSTRACT FROM AUTHOR]

Zonal occupant level is of great practical interest for building energy saving under normal operations and for fast evacuation under emergency. Though there are many existing sensing systems to estimate this information, the problem is still challenging due to the privacy concerns, the random human movement, and the accumulative error. In this paper, we consider this important problem and focus on infrared beam systems that monitor the zonal arrival and departure events. We make the following contributions. First, a rule (i.e., Rule 1) based on the stay time is developed to reduce the accumulated estimation error in each zone. Second, a rule (i.e., Rule 2) is designed to coordinate the estimation among neighboring zones. A decentralized estimation method is then developed using these two rules. Third, the advantage of this method is demonstrated through simulation results and field tests. We hope this work brings insight to zonal occupant level estimation in buildings in more general situations. [ABSTRACT FROM AUTHOR]