IMU Performance Analysis for a Pedestrian Tracker

Zheng, Jianwei; Qi, Minhui; Xiang, Kui; Pang, Muye

doi:10.1007/978-3-319-65289-4_47

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…• Regarding sensors and controllers, the possible threats were mitigated using sensors and controllers available on the market having regular features [46][47][48]. On this regard, the conclusions obtained are valid for the low-cost sensors currently available on the market, and as the technology is always improving the prior conditions, the conclusions can be applied in forthcoming low-cost sensors.…”

Section: (B) External Validitymentioning

confidence: 72%

Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

Garcia-Guzman¹,

Prieto²,

Pajares³

et al. 2018

Preprint

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Abstract:Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that lowcost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 -37€) and GPS (Mtk3339 -53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

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Section: (B) External Validitymentioning

confidence: 72%

Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

Garcia-Guzman¹,

Prieto²,

Pajares³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…(B) External validity: In the scope of this research, the factors that influence the external validity are related to the replication of this experiment. These replications must consider several relevant factors: sensors and controllers included in the experimental kits, their location in the vehicle and road conditions: Regarding sensors and controllers, the possible threats were mitigated using sensors and controllers available on the market having regular features [ 37 , 38 , 39 ]. In this sense, the conclusions obtained are valid for the low-cost sensors currently available on the market, and as the technology is always improving the prior conditions, the conclusions can be used for future low-cost sensors.…”

Section: Methodsmentioning

confidence: 99%

“…Regarding sensors and controllers, the possible threats were mitigated using sensors and controllers available on the market having regular features [ 37 , 38 , 39 ]. In this sense, the conclusions obtained are valid for the low-cost sensors currently available on the market, and as the technology is always improving the prior conditions, the conclusions can be used for future low-cost sensors.…”

Section: Methodsmentioning

confidence: 99%

VEHIOT: Design and Evaluation of an IoT Architecture Based on Low-Cost Devices to Be Embedded in Production Vehicles

Redondo

Prieto-González

Guzmán

et al. 2018

Sensors

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Nowadays, the current vehicles are incorporating control systems in order to improve their stability and handling. These control systems need to know the vehicle dynamics through the variables (lateral acceleration, roll rate, roll angle, sideslip angle, etc.) that are obtained or estimated from sensors. For this goal, it is necessary to mount on vehicles not only low-cost sensors, but also low-cost embedded systems, which allow acquiring data from sensors and executing the developed algorithms to estimate and to control with novel higher speed computing. All these devices have to be integrated in an adequate architecture with enough performance in terms of accuracy, reliability and processing time. In this article, an architecture to carry out the estimation and control of vehicle dynamics has been developed. This architecture was designed considering the basic principles of IoT and integrates low-cost sensors and embedded hardware for orchestrating the experiments. A comparison of two different low-cost systems in terms of accuracy, acquisition time and reliability has been done. Both devices have been compared with the VBOX device from Racelogic, which has been used as the ground truth. The comparison has been made from tests carried out in a real vehicle. The lateral acceleration and roll rate have been analyzed in order to quantify the error of these devices.

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“…The sensor has been gaining interest in the field of robotics, being used for instance for autonomous navigation of aerial indoor vehicles [58] or for obstacle avoidance of ground robots [59]. RTAB-Map (Real-Time Appearance-Based Mapping) 14 is a very promising RGB-D Graph-Based SLAM approach developed by Labbé and Michaud [60,61], which uses a global Bayesian loop closure detector. According to the authors, the loop closure detector uses a bag-of-words approach to determinate how likely a new image comes from a previous location or a new location.…”

Section: Preliminariesmentioning

confidence: 99%

“…where the parameter n • is the noise density (aka spectral noise density), and T is the time period over which the idealized white noise process is integrated (one hour, in our case). Finally, the turn on bias and the bias correlation time refer to the bias value, originated when the inertial sensor turns on, and its time constant, respectively [60]. As said previously, the above mentioned procedure is independently of a specific sensor.…”

Section: Imu Sensor Modelmentioning

confidence: 99%

Robot Operating System (ROS)

2020

Studies in Computational Intelligence

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This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems. The book is divided into four parts: Part 1 features two papers on navigation, discussing SLAM and path planning. Part 2 focuses on the integration of ROS into quadcopters and their control. Part 3 then discusses two emerging applications for robotics: cloud robotics, and video stabilization. Part 4 presents tools developed for ROS; the first is a practical alternative to the roslaunch system, and the second is related to penetration testing. This book is a valuable resource for ROS users and wanting to learn more about ROS capabilities and features.

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IMU Performance Analysis for a Pedestrian Tracker

Cited by 11 publications

References 6 publications

Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

VEHIOT: Design and Evaluation of an IoT Architecture Based on Low-Cost Devices to Be Embedded in Production Vehicles

Robot Operating System (ROS)

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