Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

Castaño, Fernando; Beruvides, Gerardo; Haber, Rodolfo E.; Artuñedo, Antonio

doi:10.3390/s17092109

Cited by 45 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include Gazebo [29], USARSIM [30], Microsoft Robotics Developer Studio [31], and other simulators used to interactively design and debug autonomous systems in the early stage of development. Other more recent examples include customized simulations with simplified physics for closed-loop autonomy simulations in MATLAB [32,33].…”

Section: Discussionmentioning

confidence: 99%

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

et al. 2018

View full text Add to dashboard Cite

Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation.

show abstract

Section: Discussionmentioning

confidence: 99%

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Guo et al [53] and Wen et al [54] used deep convolutional neural network for diagnosing fault. Castano et al [55] used machine learning for object detection using virtual on-chip lidar sensor.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

et al. 2020

View full text Add to dashboard Cite

Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel-Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

show abstract

“…The particular implementation of the CPS-based co-simulation framework, the LiDAR-based collaborative map is based on a co-simulation framework between two different software systems, designed in [90]. However, the contribution of this study is to include the real part in the co-simulation framework.…”

Section: Iot Lidar-based Collaborative Mapping -A Case Studymentioning

confidence: 99%

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

Self Cite

View full text Add to dashboard Cite

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

show abstract

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

Cited by 45 publications

References 26 publications

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Contact Info

Product

Resources

About