Location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched LiDAR sequential images

Heonmoo Kim,Yosoon Choi

Department of Energy Resources Engineering,Pukyong National University,Busan 48513,Republic of Korea

Keywords:Pattern matching Location estimation Autonomous driving robot 3D tunnel mapping Underground mine

ABSTRACT In this study,a machine vision-based pattern matching technique was applied to estimate the location of an autonomous driving robot and perform 3D tunnel mapping in an underground mine environment.The autonomous driving robot continuously detects the wall of the tunnel in the horizontal direction using the light detection and ranging (LiDAR)sensor and performs pattern matching by recognizing the shape of the tunnel wall.The proposed method was designed to measure the heading of the robot by fusion with the inertial measurement units sensor according to the pattern matching accuracy;it is combined with the encoder sensor to estimate the location of the robot.In addition,when the robot is driving,the vertical direction of the underground mine is scanned through the vertical LiDAR sensor and stacked to create a 3D map of the underground mine.The performance of the proposed method was superior to that of previous studies;the mean absolute error achieved was 0.08 m for the X-Y axes.A root mean square error of 0.05 m2 was achieved by comparing the tunnel section maps that were created by the autonomous driving robot to those of manual surveying.

1.Introduction

In recent years,the autonomous driving technology has attracted great interest globally,and various automobile companies are making significant efforts to commercialize it.For example,Tesla developed Autopilot,a Level-2 autonomous driving system and released it as a commercial product[1].Autopilot uses devices,such as cameras,radars,and ultrasonic sensors.Research is being conducted to actualize fully autonomous driving.Google’s parent company,Alphabet [2],developed a Level-4 self-driving taxi,Waymo,and is currently conducting Level-5 prototype tests.Furthermore,commercial products,such as General Motors’Cruise division [3] and Ford’s Argo AI [4],are being developed.Various global IT companies are also researching the autonomous driving technology [5,6].

Various studies,pertaining to autonomous driving technology,have been conducted in the mining industry [7].For example,the autonomous driving technology was used to develop equipment to improve safety in underground mines or automatically transport minerals [8,9].Yinka-Banjo et al.developed an autonomous driving system that detects hazards in underground mines [10].They analyzed the possibility of detecting rockfall or toxic gases in an underground mine using the multi-autonomous driving robot.Berglund et al.conducted a study on the optimal path for autonomous mineral transport vehicles to transport minerals while avoiding obstacles [11].Chi et al.developed an autonomous driving scrapper that combined a laser-based estimation system and barcode recognition technology and conducted a driving test on a simulated test site [12].Volvo tested an autonomous transport system in a real limestone mine [13].Studies have also been conducted to measure the environmental factors in underground mines [14–16] or explore the inside of a mine shaft [17–20] using autonomous driving robots.

Many studies have been performed to create three-dimensional(3D)maps of underground mines using light detection and ranging(LiDAR) sensors [21–24].Some studies have employed LiDAR sensors with autonomous driving robots.For example,Baker et al.combined multiple sensors to develop an autonomous driving robot that can not only perform autonomous driving but also perform tasks such as exploring the tunnel mapping environment[25].The robot can also be used in environments with poor terrain.Studies on mapping tunnels using autonomous driving robots[26]and 3D LiDAR sensors[27]and rotating LiDAR sensors[28,29]have also been conducted.

Accurate location estimation technology is essential to perform a 3D mapping of an underground mine using an autonomous driving robot.This is because to survey an underground mine using an autonomous driving robot,the robot’s 3D coordinates (X,Y,andZ)and 3D pose (roll,pitch,and yaw) should be combined with the distance data measured by the LiDAR sensor.In other industries,many studies have been conducted to accurately estimate the robots and vehicles’ location [30–32].For instances,the vehicle’s location was estimated by comparing a pre-made map created using surveying equipment such as laser scanners,and the map conducted the detection process using the LiDAR in real time,as the autonomous driving robot drove[33].Furthermore,techniques that can detect feature points,such as corners or road intersections,as the autonomous driving robots drive,have been used for location estimation [34,35].However,it is difficult to apply the location estimation methods developed in other industries to underground autonomous driving robots.First,the shape of an underground mine frequently changes due to blasting during mining;therefore,it is difficult to repeatedly use a pre-made map data.In addition,it is difficult to detect characteristic points because the walls of the underground mine tunnel are rough and irregular.Furthermore,in underground mines,it is impossible to receive signals through the global positioning system (GPS).Besides,most of the tunnels are dark,which makes it impossible to use location estimation technology based on camera sensors.

In the mining industry,a few studies have been conducted to estimate the location of autonomous driving robots.Using encoders,inertial measurement unit sensors(IMUs),and rotating LiDAR sensors,Neumann et al.developed an autonomous driving robot for underground mining[28].Kim et al.developed an autonomous driving robot for underground mines using sensors,such as IMU,LiDARs,and encoders and conducted an accuracy comparison experiment[36].However,in these existing studies,there is error accumulation because only IMU and encoder sensors were used for location estimation.Ghosh et al.performed 3D tunnel mapping in an underground mine by combining an IMU,an encoder sensor,and a rotating 2D LiDAR sensor[29].However,the robot had to stop and re-start after each scan(stop-and-go method);therefore,the exploration time was overly long.Thus,in previous studies in which 3D tunnel mapping was performed using autonomous driving robots in underground mines,position correction was achieved using only part of the point data when estimating the robot’s position by scanning the wall of the underground mine using a LiDAR sensor.However,the detailed shape of the uneven tunnel wall was not reflected,and the performance of the location estimation was degraded.

In this study,a machine vision-based pattern matching technique was applied to an autonomous driving robot for underground mining to improve the location estimation accuracy and perform the 3D mapping of the underground mine shaft.Pattern matching was performed by continuously recognizing the shape of the walls of the tunnel using horizontal LiDAR.The results from pattern matching were combined with an encoder sensor data to estimate the robot’s 3D pose and location.Through the vertical LiDAR sensor,the cross section of the underground mine tunnel was mapped and accumulated while the robot was driving to create a 3D tunnel model.The developed autonomous driving robot system was then applied to an actual underground mine site to evaluate the location estimation accuracy and its applicability to underground 3D models.

2.Materials and methods

2.1.System configuration for autonomous driving robot

The sensor and data processing procedure of the autonomous driving robot used in this study are shown in Fig.1.The autonomous driving robot utilized four types of sensors (IMU,encoder,vertical LiDAR,and horizontal LiDAR) to perform real-time autonomous driving,location estimation,and 3D mapping.To measure the robot’s three-axis pose,an IMU sensor that combined an acceleration sensor,a geomagnetic sensor,and a gyroscope sensor with a Kalman filter was used.The IMU sensor used in this study measured the robot’s three-axis pose(roll,pitch,and yaw) in the form of Euler angles.

To measure the driving distance of the robot,an encoder sensor that could measure distance using the number of rotations of the robot’s wheel was used.Wheel-type mobile robots can also calculate the heading of the robot based on the difference between the number of the rotations of the left and right wheels.However,in an underground mine environment where the terrain is very rough,error occurring due to the slip generated when rotating may increase.Consequently,the wheel-type robots were used to measure only the distance of the robot’s linear movement,and the rotation of the robot was measured using the IMU sensor and pattern matching method.

For the robot’s heading angle estimation and 3D tunnel mapping,two (horizontal,vertical) LiDAR sensors were used.The horizontal LiDAR sensor measured the distance between the left and right walls,following which it recognized the centerline of the road and ran autonomously along the line.Furthermore,the heading angle of the robot was calculated by comparing the two-point cloud data continuously inputted with a pattern matching algorithm.In addition,the vertical LiDAR sensor measured the realtime vertical section of the tunnel as the robot drove and accumulated it to create a 3D tunnel model.The detailed specifications of the sensors and controllers used in this study are shown in Table 1.

Table 1Specification of sensors,controller,driving platform used in this study.

The view of the autonomous driving robot and sensors used in this study is shown in Fig.2.A vertical LiDAR was installed on the front of the robot to scan the vertical section of the tunnel.On the top,a horizontal LiDAR that measured the horizontal wall shaft and a webcam that recorded the driving process of the robot were installed.In addition,an IMU sensor that measured the 3D pose was installed inside the robot,and an encoder sensor was installed on the robot’s wheel.The robot was encased in an acrylic case to prevent physical shock and leakage,and a laptop,which is the main controller,was placed inside the case.The robot’s main and remote controllers were wirelessly connected via Wi-Fi,and a communication environment was established with the microcontroller controlling individual motors through the RS-232 method.The robot and horizontal and vertical LiDAR have unique coordinate systems.

In this study,LabVIEW was used as the programming language to perform autonomous driving,location estimation,and 3D tunnel mapping (National Instruments,Austin,TX,USA).The user interface of the autonomous driving system developed in this study is shown in Fig.3.The user interface visualizes the webcam screen,IMU sensor data,horizontal/vertical LiDAR data,estimated location,and robot settings in real time.In addition to the parts included in the interface screen,the programming codes included autonomous driving algorithms,pattern matching,and wireless communication through remote controllers.

2.2.Location estimation and 3D tunnel mapping

2.2.1.Location estimation

Fig.1.Overall structure of data processing for the autonomous driving robot used in this study.

The pattern matching and data processing sequence of the sensors when estimating the location of the autonomous driving robot are shown in Fig.4.Pattern matching was performed using the horizontal LiDAR data(SK,SK+1)independently and measured two consecutive times (K,K+1),and the angle value representing the heading of the robot and score value representing the accuracy were outputted.A calculated score value exceeding the threshold indicated that the pattern matching was correct,and the heading value output of the pattern matching was used.In contrast,if the score value was lower than the threshold,it was determined that the pattern matching accuracy was low,and the heading value obtained from the IMU sensor was used instead.The current heading value was calculated by accumulating the heading value measured in units of 0.5 s.The location of the autonomous driving robot was estimated using the heading value obtained through pattern matching or IMU sensor and the driving distance measured by the encoder sensor.Eqs.(1),(2),and (3) represent the location estimation equation of the autonomous driving robot.whered(tk) is the driving distance of the robot at timetk;α(tk) the heading;and β(tk) the pitch angle.

Fig.2.Conceptual view of autonomous driving robot,sensors,and the coordinate systems.

2.2.2.3D tunnel mapping

The LiDAR sensor used in this study was SICK’s LMS-111 model,the field of view was 270°,and the point data were measured in 0.25° increments.Because the LiDAR sensor was measured in increments of 0.5 s,the horizontal/vertical LiDAR each collected 2162-point cloud data in 1 s.In the LiDAR,the coordinates (xpoint,ypoint) of points separated by an angle θ and a distanceDmay be defined as Eqs.(4) and (5).

To create a 3D map by stacking the point clouds measured by the vertical LiDAR,it was necessary to record information on the autonomous driving robot’s location and pose,along with the point data,as it drove.First,the 3D pose of the robot was defined by the roll,pitch,and yaw in the form of the Euler angle.They represent the degree of rotation of the robot in thex-,y-,andz-axes directions,respectively and can be represented by the rotation matrices of Eqs.(6),(7),and (8).

Fig.3.User interface of autonomous driving robot system in LabVIEW software.

Fig.4.Flowchart for estimating the location of the autonomous driving robots using pattern matching,IMU,encoder sensors.

The coordinates of the tunnel map are calculated as in Eq.(9)using the autonomous driving robot’s location,pose and point data.xmap,ymap,andzmapare the coordinates of the point data,andxrobot,yrobot,andzrobotare the coordinates of the autonomous driving robot.In order to reflect the pose of the autonomous driving robot in the point cloud data that maps the tunnel,the matrices of Eqs.(6),(7),and(8)were converted into one matrix that rotates in thez-y-xorder and calculated.In addition,xpointandypointare the coordinates of the point measured by the vertical LiDAR sensor,andxLiDAR,yLiDAR,andzLiDARrepresent the moving distance to match the center position of the robot and the position of the LiDAR sensor.The tunnel coordinates calculated through Eq.(9)are stacked while the robot is driving to create a 3D model of a single underground mine tunnel.

The point data,including the location,pose,and LiDAR’s distance information obtained at timeKcan be expressed in the form of (SK) in Eq.(10).

2.3.Pattern matching of sequential LiDAR images for estimating robot’s heading

In this study,the heading of the robot was measured through pattern matching.The pattern matching technology is a machine vision-based image recognition,learning,and processing algorithm that can calculate angle and accuracy by comparing the learned template image with a target image.The pattern matching algorithm used was pyramid matching,a pattern matching algorithm that reduces the size of a sample through the Gaussian pyramid,adjusting the resolution through the Gaussian filter and minimizing the size of the image by repeating this process[37].By optimizing the data size,the total amount of computation was reduced significantly;therefore,the algorithm can be used in a short cycle[38].In this study,the Vision Assistant of National Instruments’machine vision processing software was used in conjunction with LabVIEW to implement the pattern matching technology[39].Pattern matching was performed by designating some of the previous data as template image and the current data as target image among the continuously acquired LiDAR sensor data.This process was continuously repeated to output the accuracy of pattern matching and the heading angle of the robot.

The pattern matching,as performed through the tunnel measured by the LiDAR sensor,is shown in Fig.5.The proposed pattern-matching-based heading measurement method was performed through a 2D LiDAR that measured the wall in the horizontal direction;there was no additional sensor.Among the point data continuously measured by the LiDAR sensor as the robot drove,the rotational angle of the region of interest (ROI) was measured by comparing the overlapping points of the mine wall data (SK) at timeKand mine wall data (SK+1) at timeK.Similarly,SK+1was learned as a template image ofSK+2,and the pattern matching betweenSK+1andSK+2was sequentially performed.When comparing the ROI measured inSKwith the tunnel data ofSK+1,the matching location was automatically found and the rotation angle was measured,as shown in Fig.4.

The data processing sequence of the proposed pattern matching method is shown in Fig.6.After removing the singular values caused by sensor error among 1081 points cloud data (SK) measured by the horizontal LiDAR at timeK,the point cloud data on the tunnel wall was converted into an RGB image.To apply the RGB image to the machine vision-based pattern-matching algorithm,the grayscale image was converted,and the 2-m point in front of the robot was selected as the ROI and learned as a template pattern.Then,the learned template pattern was applied toSK+1,pattern matching was performed,and the score value and angle were calculated.The score value indicated the pattern matching accuracy.As it had a value between 0 and 1000 points,it could be expressed as a value from 0% to 100%.The angles represented the robot’s rotating angle,that is,its heading change.

When the size of the template wasK×Land the size of the image wasM×Nbetween two consecutively inputted tunnel images,the correlation at point(i,j)was calculated using Eq.(11).

wherei=0,1,2,...M-1 andj=0,1,2,...N–1 The valueC(i,j)at the highest point of the value,up toN-1,could be used to calculate the pattern matching score.The constantsrelated to the intensity of light could be ignored because the performance of the LiDAR sensor was constant,regardless of it.

Fig.5.Pattern matching using continuous two LiDAR data (SK, SK+1).

The pattern matching success and failure in the LabVIEW software are shown in Fig.7.The score value was calculated using Eq.(11);failure was determined based on the threshold value.

2.4.Field experiment

2.4.1.Study area

In this study,an abandoned underground mine located in Ulsan,Korea (N35°32′4′′,E129°5′37′′) was set as a research area to estimate the location of an autonomous driving robot and map the 3D tunnel.To perform the location estimation and tunnel mapping experiment,a 25-m long and 3-m wide area was designated the test area,as shown in Fig.8.Then,five points of the entire experimental site were selected as SectionsA,B,C,D,andE,and the locations of the robot,webcam display,and tunnel wall were compared.Further,sticky notes were attached to the floor at regular intervals to accurately observe the autonomous driving robot’s driving path.In the test area,all the tunnel walls were within a range that could be detected by the LiDAR sensor.

2.4.2.Experiment method

The autonomous driving robot received a signal wirelessly from the remote controller at the starting point.Then,it autonomously drove through the mine at a constant speed along the central point of the tunnel via an autonomous driving algorithm using a horizontal LiDAR sensor;pattern matching was performed by comparing the shape change of the tunnel wall.Additionally,the 3D tunnel map of the underground mine was created using the estimated location and tunnel data measured by the vertical LiDAR sensor in addition to the estimated location coordinates and the point data measured by the vertical LiDAR sensor.All the sensor data were measured and saved every 0.5 s.After the experiment,the accuracy of the location estimation was evaluated in comparison with the robot’s actual location.

3.Results

In Fig.8,an autonomous driving robot,performing location estimation and 3D tunnel mapping while autonomously driving in an underground mine is shown.In each section,there was a webcam screen and a tunnel image measured by a horizontal LiDAR.As the robot was driving,its pose and location were estimated using the heading and distance data measured by the sensors in real time.The autonomous driving robot could confirm the safety of driving in the underground mine shaft;it took approximately 63 s to drive through the entire experimental section.It was confirmed that the pattern matching failed or its accuracy decreased in the section where the robot had to rotate at a relatively large angle,compared to other sections,as shown in Fig.9b.

Fig.10 shows the score of the pattern matching performed by the autonomous driving robot in the experiment.Pattern matching and location estimation were performed every 0.5 s while the robot was driving and approximately 130 times during the entire experiment.Pattern matching was successful at all points;however,the ratios of success and failure were recorded at 64.8% and 35.2%,respectively when a threshold of 70%was applied to the pattern matching score.The heading variations indicate the difference in the heading between the previous and present points;it was calculated as the average of the absolute values to indicate only the degree of change.The overall average was approximately 2.5°,indicating an average rotation of 2.5° in the (+) or (-) direction every 0.5 s.On the other hand,when pattern matching was successful with an accuracy of 70% or more,the heading value was approximately 1.92°and 3.05°,when it failed.It was found that the pattern matching accuracy was high when the robot rotated at a small angle compared to the previous point of view,and low,when the robot rotated at a relatively large angle.At some points,the heading variations showed significant deviations exceeding 10°,and it was confirmed that the pattern matching accuracy was very low at these points.

Fig.6.Procedure of obtaining pattern-matching data from horizontal LiDAR sensor.

Fig.7.Comparison of pattern matching success and failure in LabVIEW software.

Fig.8.Conceptual diagram of field experiment in underground mine environment.

In some areas of the underground mine shaft,there was a curved section in which the robot had to rotate at a relatively large angle.When the robot encountered the bend,the heading value changed significantly,and the robot maintained the heading while driving until the next bend.It was confirmed that the success rate of pattern matching was relatively higher in the curve,compared to at the entrance of the curved point,even when the robot maintained a large heading value when entering the curved section.

Fig.11 shows the estimated driving path of the autonomous driving robot in the underground mine shaft.Overall,it was confirmed that the robot was driven in a path similar to the central point of the road.Because the location estimation was performed in a short cycle,it was confirmed that the movement path of the robot was formed relatively densely.

Fig.9.View of autonomous driving robot,webcam display,tunnel wall measured by the LiDAR sensor at Section A,Section B,Section C,Section D,Section E.

Fig.10.Pattern matching score recorded as autonomous robot drove and 70%score line.

Fig.12 shows the 3D map created by stacking vertical LiDAR data as the autonomous driving robot drove through the underground mine tunnel.Over a total distance of 25 m,the robot moved approximately 25 m in theX-axis direction and 6 m in theY-axis direction.The underground mine shaft was approximately 2.41-m high in theZ-axis direction.Because the robot was driving at a low speed and mapping was performed every 0.5 s,the shaft was densely displayed.

Fig.11.Driving path of autonomous driving robot in underground mine.

4.Discussion

In this study,the location of an autonomous driving robot was estimated using a pattern matching method;IMU sensors were combined according to the pattern matching score.In addition,the 3D map was created based on the location of the robot and vertical section of the underground mine shaft measured from the vertical LiDAR.To evaluate the accuracy of the location estimation and 3D mapping methods developed in this study,a quantitative comparison between the estimated robot location,3D tunnel shape,and actual value must be performed.The accuracy of the proposed pattern-matching-based location estimation method was quantitatively evaluated.As the robot drove through the underground mine,the evaluation method estimated its location using the pattern-matching-based location estimation method and the previously used location estimation method and compared it with the actual movement path.The previously used location estimation method combined the LiDAR,IMU,and encoder sensors[25–29,36].The IMU and encoder sensors performed the same function,and the robot’s pose and location were estimated by comparing the featured scanned at two different times among the LiDAR sensors’ data.

Fig.12.3D model of underground mine generated by pattern matching-based autonomous driving robot:top view,side view,front view.

Fig.13.Autonomous driving robot’s driving path estimated by both location estimation methods (LiDAR+IMU+encoder+pattern matching and LiDAR+IMU+encoder) and actual driving path in field experiment.

Fig.13 shows the results of a comparison experiment on the accuracy of the located estimation for the underground mines using the existing location estimation method and the pattern matching-based one.The existing method tended to exhibit a relatively high error in the curved section.Furthermore,the location estimation performance of the pattern-matching-based method was relatively high,with the error remaining almost constant in the entire section.

The comparison results of the proposed pattern-matchingbased location estimation method and existing ones used are shown in Table 2.The method combining pattern matching,IMU,and encoder sensors yielded an average error of 0.10 m on theX-axis,0.06 m on theY-axis,and an overall error of 0.08 m.The existing method exhibited an average error of 0.16 m on theXaxis,0.14 m on theY-axis,and approximately error 0.15 m overall.Therefore,it was proven that the proposed pattern-matchingbased location estimation method outperformed the existing method.

Table 2Experiment results of both autonomous driving robot’s location estimation methods.

Table 3Experiment results of the mapping and surveying conducted by the autonomous driving robot of the tunnel section.

Fig.14.3D point cloud map generated and survey conducted by the autonomous driving robot:top,front,and side views.

The location estimation method based on the LiDAR data used in the previous study corrected the location using only some featured points among the entire point data.However,it is often difficult to recognize the shape of the tunnels using only some point data because the walls of the tunnels are rough or irregularly formed.Consequently,it could be expected that the relative location estimation accuracy was inferior because of the high dependence on the IMU and encoder sensors.On the other hand,the pattern matching method used in this study converted the highresolution LiDAR data into a high-quality image format and calculated the heading,which enabled it to efficiently predict the robot’s pose and location,even in the underground mine environment.

In this study,3D mapping,which was performed using a method based on pattern-matching location estimation,and a vertical LiDAR sensor were evaluated quantitatively.Standard surveying instruments,such as a “total station” was used for surveying five points among the entire experimental area where tunnel mapping was performed using the autonomous robot.Furthermore,the cross sections of the shaft map constructed using the robot and those obtained using standard measurement methods were quantitatively compared.To match the scan points of the vertical LiDAR mounted on the autonomous driving robot with those of standard survey methods,a 3D tunnel model was constructed for the experimental area,and the sections matching the standard survey points were compared in a cross-sectional manner.

Fig.14 shows the 3D point cloud map constructed by the autonomous driving robot and surveying.Five points between 4 and 10 m of the experimental area were surveyed,and the constructed map was compared with the robot-based mapping results.The top view (Fig.14a) confirmed that the map created by the robot was distributed similar to the result of the actual survey point in thex-andy-axis directions.Furthermore,the front (Fig.14b) and side views (Fig.14c) confirmed that the floor and ceiling of the tunnel were mapped with high accuracy.

Fig.15.Comparison of the 2D tunnel sections generated and survey conducted at five points (a,b,c,d,and e) by the autonomous driving robot.

Fig.15 shows the results of comparing the 2D tunnel sections generated by the autonomous driving robot and those surveyed manually.It can be seen that the 2D tunnel sections created by the two methods are similar.Table 3 lists a quantitative comparison of the tunnel sections created and survey conducted by the autonomous driving robot.For the five survey points,the area and accuracy of the tunnel sections generated by the autonomous driving robot were calculated by comparing it with that of the actual survey.The map created by the autonomous driving robot achieved an absolute error with a minimum of 0.0075 m2up to 0.1069 m2and a root mean square error (RMSE) value of 0.05 m2.The LiDAR sensor mounted on the autonomous robot exhibited relatively high accuracy;therefore,if the location of the autonomous robot was accurately estimated,the accuracy of the tunnel mapping would also improve.

5.Conclusions

In this study,the autonomous driving robot’s location estimation and 3D tunnel mapping were performed using a machinevison-based pattern matching technique.

(1) The proposed location estimation method recognized and matched the shape of the tunnel wall using a horizontal LiDAR sensor to measure the heading angle of the robot.When the pattern matching accuracy was poor,the heading was measured using an IMU sensor.The robot’s pose and location were estimated using the heading and the encoder sensor,which can measure the distance and IMU;the estimates were combined with the vertical LiDAR sensor data to create a 3D tunnel.The accuracy of the proposed and previous location estimation methods was compared;the accuracy achieved by the proposed method was as high as 0.07 m.In addition,the tunnel mapping conducted using the autonomous driving robot exhibited an RMSE of 0.05 m2.

(2) The 3D maps of underground mines created in previous studies using autonomous driving robots tended to accumulate errors significantly because of the types of sensors used,such as the IMU and encoder.In studies in which location was measured using distance-measuring sensors,such as LiDAR sensors,it was necessary to conduct prior exploration,which posed an inconvenience when used in a form that performed comparison with survey points or existing maps.Further,it was difficult to detect the feature points due to the topographical characteristics of underground mines;another limitation was that the amount of computation increased because it was necessary to identify the shape of each wall.The proposed pattern-based location estimation method could be effectively utilized in underground mining environments and was highly effective at minimizing the location estimation error of the robot because it can be performed in a short cycle.Furthermore,the proposed method reduces the amount of computation through image optimization.As above-mentioned,the fact that it could be performed in a short cycle enabled highly accurate location estimation.

(3) This study measured the angle in the yaw direction using a horizontal 2D LiDAR sensor;however,the angles in the roll and pitch directions were measured using only the IMU sensor.If pattern matching in the yaw direction and the roll and pitch directions are performed simultaneously,the accuracy of the robot’s 3D pose measurement will increase,and the precision of the 3D mapping for the underground mine shaft will also be guaranteed.Therefore,research on three-axis pose correction using 3D LiDAR or a vision camera is necessary.In addition,the chosen experimental area was a straight-line shaft that was 25 m long with no obstacles along the direction of driving.However,in actual underground mines,narrow sections,intersections,obstacles,and workers exist as obstacles.Therefore,in future works,autonomous driving algorithms,such as obstacle avoidance and path planning,should be supplemented to incorporate such field conditions.

(4) In this study,field experiments were performed using a small autonomous robot;however,in actual underground mines,there may be areas with poor road conditions.Therefore,it is necessary to improve the driving performance by applying the autonomous driving algorithm that was developed in this study to the robot platform to enable it to safely navigate in a field environment.

Underground mines have a variety of risk factors such as rock collapse and toxic gases.Therefore,many areas that are too dangerous for humans to access.Using autonomous driving robots in these areas can increase the safety of exploration.Furthermore,transporting equipment in underground mines using the autonomous driving system can significantly boost the productivity of the mine.To use autonomous driving robots in these areas,accurate location estimation and 3D mapping are essential.It is expected that this study will serve as an important reference material in various fields related to location estimation and 3D mapping of autonomous driving robots in underground mines in the future.

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF)grant funded by the Korea government(MSIT) (No.2021R1A2C1011216).