scholarly journals Comparison of Machine Vision Algorithms used by Autonomous Driving Robots to Recognize Road Signs in Underground Mines

2021 ◽  
Vol 58 (6) ◽  
pp. 559-567
Author(s):  
Heonmoo Kim ◽  
Yosoon Choi
Keyword(s):  
Machine Vision ◽  
Autonomous Driving ◽  
Underground Mines ◽  
Road Signs

scholarly journals Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

2021 ◽  
Vol 11 (21) ◽  
pp. 10235
Author(s):  
Heonmoo Kim ◽  
Yosoon Choi
Keyword(s):  
Machine Vision ◽  
Autonomous Driving ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Underground Mines ◽  
Driving Mode ◽  
Road Signs ◽  
Sign Recognition ◽  
Road Sign ◽  
Road Sign Recognition

In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.

scholarly journals Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving

2021 ◽  
Vol 10 (11) ◽  
pp. 761
Author(s):  
Tengfei Yu ◽  
He Huang ◽  
Nana Jiang ◽  
Tri Dev Acharya
Keyword(s):  
Accuracy Assessment ◽  
A Priori ◽  
Autonomous Driving ◽  
Relative Accuracy ◽  
Accuracy Evaluation ◽  
High Definition ◽  
Verification Method ◽  
Road Signs ◽  
Point Set ◽  
Priori Information

High-definition maps (HDM) for autonomous driving (AD) are an important component of AD systems. HDMs accurately provide a priori information, including lane lines, and road signs, for AD systems. It is an important task to make a reasonable accuracy assessment of the HDM. The current methods for relative accuracy evaluation of general maps in the field of mapping are not fully applicable to HDMs. In this study, a method based on point set alignment and resampling is used to evaluate the relative accuracy of lane lines, and experiments are conducted based on relevant real HDM data. The results show that the relative accuracy of the lane lines is more detailed and relevant than the traditional method. This has implications for the quality control of HDM production.

scholarly journals Comparison of Three Location Estimation Methods of an Autonomous Driving Robot for Underground Mines

2020 ◽  
Vol 10 (14) ◽  
pp. 4831
Author(s):  
Heonmoo Kim ◽  
Yosoon Choi
Keyword(s):  
Autonomous Robots ◽  
Estimation Error ◽  
Autonomous Driving ◽  
Underground Mine ◽  
Location Estimation ◽  
Underground Mines ◽  
Estimation Methods ◽  
Measurement Unit ◽  
Average Error ◽  
Average Estimation Error

In this study, we compared the accuracy of three location estimation methods of an autonomous driving robot for underground mines: an inertial measurement unit with encoder (IMU + encoder) sensors, Light Detecting and Ranging with encoder (LiDAR + encoder) sensors, and IMU with LiDAR and encoder (IMU + LiDAR + encoder) sensors. An accuracy comparison experiment was conducted in an indoor laboratory composed of four sections (X-change, X-Y change, X-Z change, and Y-change sections) that simulated an underground mine. The robot’s location was estimated using each of the three location estimation methods as the autonomous driving robot moved, and the results accuracy was analyzed by comparing the estimated location with the robot’s actual location. From the results of the indoor experiments, the average estimation error of the IMU + LiDAR + encoder sensors was approximately 0.09 m, that of the IMU + encoder was 0.19 m, and that of the LiDAR + encoder was 0.81 m. In a field experiment, the average error of the IMU + LiDAR + encoder was approximately 0.11 m, that of the IMU + encoder was 0.17 m, and that of the LiDAR + encoder was 0.70 m. In conclusion, the IMU + LiDAR + encoder method, which uses three types of sensors, showed the highest accuracy in estimating the location of autonomous robots in an underground mine.

scholarly journals Real-Time Road Lane Detection in Urban Areas Using LiDAR Data

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 276 ◽  
Author(s):  
Jiyoung Jung ◽  
Sung-Ho Bae
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Autonomous Driving ◽  
Lane Detection ◽  
Detection Methods ◽  
Lidar Data ◽  
The Road ◽  
Road Signs ◽  
Probe Vehicle ◽  
Map Generation

The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the ground. Given a 3D point cloud scanned by a 3D LiDAR sensor, we categorized the points of the drivable region and distinguished the points of the road signs on the ground. Then, we developed an expectation-maximization method to detect parallel lines and update the 3D line parameters in real time, as the probe vehicle equipped with the LiDAR sensor moved forward. The detected and recorded line parameters were integrated to build a lane-level digital map with the help of a GPS/INS sensor. The proposed system was tested to generate accurate lane-level maps of two complex urban routes. The experimental results showed that the proposed system was fast and practical in terms of effectively detecting road lines and generating lane-level maps.

scholarly journals Performance of Mobile LiDAR in Real Road Driving Conditions

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7461
Author(s):  
Jisoo Kim ◽  
Bum-jin Park ◽  
Chang-gyun Roh ◽  
Youngmin Kim
Keyword(s):  
Environmental Changes ◽  
Substantial Reduction ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Sensor Data ◽  
Road Signs ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Object Material ◽  
Vehicle Sensor

The performance of LiDAR sensors deteriorates under adverse weather conditions such as rainfall. However, few studies have empirically analyzed this phenomenon. Hence, we investigated differences in sensor data due to environmental changes (distance from objects (road signs), object material, vehicle (sensor) speed, and amount of rainfall) during LiDAR sensing of road facilities. The indicators used to verify the performance of LiDAR were numbers of point cloud (NPC) and intensity. Differences in the indicators were tested through a two-way ANOVA. First, both NPC and intensity increased with decreasing distance. Second, despite some exceptions, changes in speed did not affect the indicators. Third, the values of NPC do not differ depending on the materials and the intensity of each material followed the order aluminum > steel > plastic > wood, although exceptions were found. Fourth, with an increase in rainfall, both indicators decreased for all materials; specifically, under rainfall of 40 mm/h or more, a substantial reduction was observed. These results demonstrate that LiDAR must overcome the challenges posed by inclement weather to be applicable in the production of road facilities that improve the effectiveness of autonomous driving sensors.

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision
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