Miniaturization and sensor fusion of a measurement unit for a trailing bomb

2012 ◽  
Author(s):  
J. Popelka ◽  
P. Paces
Keyword(s):  
Sensor Fusion ◽  
Measurement Unit

Development and Experimental Verification of a Person Tracking System of Mobile Robots Using Sensor Fusion of Inertial Measurement Unit and Laser Range Finder for Occlusion Avoidance

2021 ◽  
Vol 33 (1) ◽  
pp. 33-43
Author(s):  
Kazuhiro Funato ◽  
Ryosuke Tasaki ◽  
Hiroto Sakurai ◽  
Kazuhiko Terashima ◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Sensor Fusion ◽  
Experimental Verification ◽  
Inertial Measurement Unit ◽  
Tracking System ◽  
Measurement Unit ◽  
Laser Range Finder ◽  
Range Finder ◽  
Inertial Measurement ◽  
Laser Range

The authors have been developing a mobile robot to assist doctors in hospitals in managing medical tools and patient electronic medical records. The robot tracks behind a mobile medical worker while maintaining a constant distance from the worker. However, it was difficult to detect objects in the sensor’s invisible region, called occlusion. In this study, we propose a sensor fusion method to estimate the position of a robot tracking target indirectly by an inertial measurement unit (IMU) in addition to the direct measurement by an laser range finder (LRF) and develop a human tracking system to avoid occlusion by a mobile robot. Based on this, we perform detailed experimental verification of tracking a specified person to verify the validity of the proposed method.

scholarly journals Research on Lane a Compensation Method Based on Multi-Sensor Fusion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1584 ◽  
Author(s):  
Yushan Li ◽  
Wenbo Zhang ◽  
Xuewu Ji ◽  
Chuanxiang Ren ◽  
Jian Wu
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Compensation Method ◽  
Measurement Unit ◽  
Vision Sensor ◽  
Vision Sensors ◽  
Cubic Polynomial ◽  
Vehicle Test ◽  
Yaw Angle ◽  
Geometric Relationship

The curvature of the lane output by the vision sensor caused by shadows, changes in lighting and line breaking jumps over in a period of time, which leads to serious problems for unmanned driving control. It is particularly important to predict or compensate the real lane in real-time during sensor jumps. This paper presents a lane compensation method based on multi-sensor fusion of global positioning system (GPS), inertial measurement unit (IMU) and vision sensors. In order to compensate the lane, the cubic polynomial function of the longitudinal distance is selected as the lane model. In this method, a Kalman filter is used to estimate vehicle velocity and yaw angle by GPS and IMU measurements, and a vehicle kinematics model is established to describe vehicle motion. It uses the geometric relationship between vehicle and relative lane motion at the current moment to solve the coefficient of the lane polynomial at the next moment. The simulation and vehicle test results show that the prediction information can compensate for the failure of the vision sensor, and has good real-time, robustness and accuracy.

scholarly journals Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 46 ◽  
Author(s):  
N. Koksal ◽  
M. Jalalmaab ◽  
B. Fidan
Keyword(s):  
Sensor Fusion ◽  
Sensor Data ◽  
Measurement Unit ◽  
Linear Quadratic ◽  
Attitude Tracking ◽  
Control Scheme ◽  
Controller Performance ◽  
Sensor Measurement ◽  
Measurement Noises ◽  
Quadratic Tracking

In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking solution is combined with an online least squares based parameter identification scheme to estimate the instantaneous inertia of the quadrotor. Sensor measurement noises are also taken into account for the on-board Inertia Measurement Unit (IMU) sensors. To improve controller performance in the presence of sensor measurement noises, two sensor fusion techniques are employed, one based on Kalman filtering and the other based on complementary filtering. The ALQT controller performance is compared for the use of these two sensor fusion techniques, and it is concluded that the Kalman filter based approach provides less mean-square estimation error, better attitude estimation, and better attitude control performance.

scholarly journals Moving Horizon Estimation for GNSS-IMU sensor fusion

2020 ◽  
pp. 112-122
Author(s):  
Guido Sánchez ◽  
Marina Murillo ◽  
Lucas Genzelis ◽  
Nahuel Deniz ◽  
Leonardo Giovanini
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Satellite System ◽  
Measurement Unit ◽  
Moving Horizon Estimation ◽  
Ground Vehicle ◽  
Global Navigation Satellite ◽  
Time Systems ◽  
True Values

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

scholarly journals A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 840
Author(s):  
Lianwu Guan ◽  
Xiaodan Cong ◽  
Qing Zhang ◽  
Fanming Liu ◽  
Yanbin Gao ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Urban Areas ◽  
Inertial Measurement Unit ◽  
Integrated Management ◽  
Small Diameter ◽  
Cost Effective ◽  
Measurement Unit ◽  
Comprehensive Review ◽  
Inertial Measurement ◽  
Defects Inspection

It is of great importance for pipeline systems to be is efficient, cost-effective and safe during the transportation of the liquids and gases. However, underground pipelines often experience leaks due to corrosion, human destruction or theft, long-term Earth movement, natural disasters and so on. Leakage or explosion of the operating pipeline usually cause great economical loss, environmental pollution or even a threat to citizens, especially when these accidents occur in human-concentrated urban areas. Therefore, the surveying of the routed pipeline is of vital importance for the Pipeline Integrated Management (PIM). In this paper, a comprehensive review of the Micro-Inertial Measurement Unit (MIMU)-based intelligent Pipeline Inspection Gauge (PIG) multi-sensor fusion technologies for the transport of liquids and gases purposed for small-diameter pipeline (D < 30 cm) surveying is demonstrated. Firstly, four types of typical small-diameter intelligent PIGs and their corresponding pipeline-defects inspection technologies and defects-positioning technologies are investigated according to the various pipeline defects inspection and localization principles. Secondly, the multi-sensor fused pipeline surveying technologies are classified into two main categories, the non-inertial-based and the MIMU-based intelligent PIG surveying technology. Moreover, five schematic diagrams of the MIMU fused intelligent PIG fusion technology is also surveyed and analyzed with details. Thirdly, the potential research directions and challenges of the popular intelligent PIG surveying techniques by multi-sensor fusion system are further presented with details. Finally, the review is comprehensively concluded and demonstrated.

Multi-Sensor Integrated Navigation in Urban and Indoor Environments

2018 ◽  
pp. 397-442 ◽  
Author(s):  
Mohamed Atia
Keyword(s):  
Sensor Fusion ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Integrated Navigation ◽  
Satellite Systems ◽  
Tightly Coupled ◽  
Sensor Processing ◽  
Global Navigation Satellite ◽  
Dense Urban Environment

The art of multi-sensor processing, or “sensor-fusion,” is the ability to optimally infer state information from multiple noisy streams of data. One major application area where sensor fusion is commonly used is navigation technology. While global navigation satellite systems (GNSS) can provide centimeter-level location accuracy worldwide, they suffer from signal availability problems in dense urban environment and they hardly work indoors. While several alternative backups have been proposed, so far, no single sensor or technology can provide the desirable precise localization in such environments under reasonable costs and affordable infrastructures. Therefore, to navigate through these complex areas, combining sensors is beneficial. Common sensors used to augment/replace GNSS in complex environments include inertial measurement unit (IMU), range sensors, and vision sensors. This chapter discusses the design and implementation of tightly coupled sensor fusion of GNSS, IMU, and light detection and ranging (LiDAR) measurements to navigate in complex urban and indoor environments.

Sign in / Sign up

Export Citation Format

Share Document