Occupancy Grid based Urban Localization Using Weighted Point Cloud

Lindong Guo1, Ming Yang1, Bing Wang1 and Chunxiang Wang2

Abstract— Localization is considered as a key capability forautonomous vehicles act in urban environments. Though havebeen proved to be able to perform convictive results, localizationmethods using neither laser scanners nor vision sensors couldachieve the goal about balancing between accuracy and cost.In this paper, an occupancy grid based localization frameworkis presented in order to obtain a precise positioning result withrelatively low-cost sensor configuration in large scale urbanenvironment. The proposed approach takes a prebuilt globalgrid map as prior knowledge for localization. Model basedfeature extraction method is introduced to provide laser pointsclassification, with each extracted point allocated a specifiedweight to describe local characteristic. The prior grid map isgenerated from weighted point cloud to be able to describethe local metric features such as curbs and building facades.Localization function is then carried out with a weight pointbased maximum likelihood matching method to determine thecorrespondence between local point cloud and the reference gridmap. There are also position initialization and reference mapmanagement modules to make the framework more practicaland reliable. In the end, the proposed approach has beenvalidated by promising experimental results with long distancetests in large urban environments.

I. INTRODUCTION

The precise localization of ego vehicle is considered as thefundamental issue for an autonomous car. Basic functionssuch as navigation and path planning as well as many othersare carried out depending on the positioning result. Ap-proaches which locate the vehicle using a prebuilt sensor mapis more reliable and accurate when compared with Simulta-neous Localization and Mapping (SLAM) based methods.Map based localization method is becoming more practicaland economic benefiting from the rapid growth of digitalmap database and sensor technology. As a consequence ofthe reasons mentioned above, this kind of method has beenwell developed with different strategies using variant typesof sensors. However, issues of reaching an accurate resultunder a relatively lower cost sensor configuration still existsin practical applications, especially when applied to largescale environments.

Most attempts of map based vehicle localization wereaddressed with the use of laser scanners. Vu et al. [1] applieda horizontal 2D laser scanner in the front of the vehicle to

This work was supported by the National Natural Science Founda-tion of China (91420101), International Chair on automated driving ofground vehicle, and National Magnetic Connement Fusion Science Program(2012GB102002).

1Lindong Guo, Ming Yang and Bing Wang are with the Key Laboratoryof System Control and Information Processing, Ministry of Education, De-partment of Automation, Shanghai Jiao Tong University, 200240 Shanghai,China mingyang@sjtu.edu.cn

2Chunxiang Wang is with the Robotics Institute, School of MechanicalEngineering, Shanghai Jiao Tong University, 200240 Shanghai, China

obtain occupancy grid as an transplant from indoor robotics,but performance of this kind of methods were closely relatedto the surroundings and height of sensors. Different fromusing horizontal laser scanners, Suganuma et al. [2] andBaldwin et al. [3] both tended to forming point cloud usingpush broom like methods, with grid map generated from theobtained 3D points and extracted features. Suzuki et al. [4]even formed 3D voxel grid to execute 6D localization, whichwas accurate but also time consuming. Xie et al. [5] returnedto 2D occupancy grids and gave a complete framework withpose initialization method, but four Ibeo laser scanners wereused which was costly.

Recent approaches tended to improve urban localizationwith considering the map representation and matching strate-gies. Wiest et al. [6] presented a region descriptor for gridmap. Hata et al. [7] and [8] tried to extract curbs and roadmarkers to be applied for localization, though proved tohave good performance, the proposed approach still sufferedfrom roads with few traffic markers. There were also re-searches using extended representation of occupancy gridsto describe more information such as trees, pillars or facades[9] [10] [11], but high performance laser scanners suchas Velodyne HDL-64 or Riegl VMX-250 LIDAR systemwere still requisite for obtaining accurate and reliable pointclouds. In contrast to use laser scanner for both mappingand localization, Wolcott et al. [12] provided an extensionof point cloud formed occupancy grid using reflectivitymaps, then used vision based method with GPU to performmatching, however, a high cost sensor configuration wasnecessary for gaining such high resolution maps. Differentstrategy was carried out by Steder et al. [13] to do MonteCarlo Localization on Open Street Map (OSM) dependingon classification of road segmentations or intersections, butthe accuracy left a lot to be improved. Method that workedwith practical system also had been developed by Alsayedet al. [14], but the use of ordinary occupancy grids still needto be considered.

As a consequence of reviewing the existing approaches,there is a strong desire of suitable map representation, whichis the key of urban localization that determine the perfor-mance and complexity. Meanwhile, the sensor configurationrestricts the practicability of the method, which is also con-siderable. In order to achieve the goal of precise localizationin large scale environment with low cost sensor configurationand lower computational complexity, we presented here anoccupancy grid based localization method. It was designedfor relatively low price laser scanners such as low-end3D Lidar or 2D laser scanner, with highly individualizedmethod to generate the occupancy grid map and matching

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016

978-1-5090-1889-5/16/$31.00 ©2016 IEEE 62

the correspondence between observation and reference map.Map management was also contained in the system to reducethe need of computational resources by accessing map filesstored in hard disk when needed. The aim of the proposedmethod is to provide a map of enough information anda suitable localization method instead of using high-endlaser scanners with map of large data amount and complexalgorithm.

The rest of this paper is organized as follows. Section IIgives the brief description of the system framework. SectionIII introduces the sensor data processing of point cloudweighting and occupancy grid generation. The localizationmethod with pose initialization and maximum likelihoodmatching is given in Section IV. Then experimental resultswith real datasets is discussed in Section V. Conclusions andfuture work are outlined in the last section.

II. SYSTEM FRAMEWORK

The proposed localization approach is a complete frame-work for autonomous vehicles based on laser scanner,odometry and GPS information. The general flowchart oflocalization procedure is shown in Figure 1. Architectureof the implementation could be briefly separated into twoparts, mapping and localization, each in the dashed line boxrespectively. There are three key functions among the blocksillustrated in the framework, point processing, occupancygrid generation and maximum likelihood matching, whichwill be detailed in Section III and IV.

The first part of the framework before localization couldbe executed is the offline mapping stage, which generates themap used for online localization. RTK-GPS is used here tocooperate with odometry observation of both wheel encoderand IMU. The trajectory filter fuses the measurements,results in a smooth path of the vehicle, therefore extractedweighted point clouds of each frame could be projectedwith a global consistent trajectory. Then Occupancy gridis generated and stored in hard disk with its correspondingglobal position.

The localization section is more complex than mapping.Laser data and odometry information are firstly processedwith the motion model to generate local points map, thenform the weighted point cloud. Weighted points map isavailable for occupancy grid generation of mapping stage, aswell as maximum likelihood matching and pose initializationof localization stage. Judgment of whether pose initializationis needed would be carried out before matching procedurewhen starting localization. After an estimation is obtained, itis used to correct the pose prediction from motion model, andaccomplish the main loop of localization. Map managementblock acts as an bridge between global map and localreference map. Position estimation is considered to find themost correlative occupancy grid map, which is provided tothe matching block as a reference.

III. SENSOR DATA PROCESSING

High definition laser scanner (for instance Velodyne HDL-64 or HDL-32) based approaches such as feature extraction

Fig. 1. General framework of the proposed occupancy grid basedlocalization method.

and matching with only one single observation frame areno longer easy to accomplish when adapting only lowerrank scanners. Therefore relevant features are required formap based localization to obtain reliable results. In orderto perform better description of the urban environmentalcharacteristic rather than using the raw measurements of laserscanner, points based weighting methods are adopted. Thefollowing instructions sets Velodyne VLP-16 as the exampleto illustrate the weighting and occupancy mapping methods,only the lower eight beams of laser scanner is used to carryout the point cloud weighting. It should be noted that 2Dlaser scanners are still suitable for such procedure, only needcertain adjustment on the specific models and functions. Theglobal point cloud mapping and occupancy grid generationis the same as using 3D laser scanners such as VelodyneVLP-16.

A. Weighted point cloud

Obstacle such as curbs and building facades are helpfulfeatures for localization, which could be extracted thoughseveral ways. In [7] a Ring Compression Analysis based curbdetection method was proposed to detect curb-like featureswith filters to reduce false positives. The proposed weightedpoint cloud generation method draw on the experience of thementioned method to perform road surface elimination andintroduces weight factors to enhance the remained features.Details of the weighted point cloud generator are providedin the following subsections.

1) Featured point cloud extraction: Illustration of pointcloud observation is shown in Figure 2. Laser observationsare modeled with rings intercepting a flat plane on whichfeature point extraction are based on. The regular distancebetween laser rings within a flat plane surface could be

63

Fig. 2. Multilayer laser rings intercepting a flat plane.

determined by

rplanei =h

tan θi= h cot θi (1)

where ri is the i th ring, h is the height of the laser scannerrelative to ground plane, and θi is the pitch angle of i th laserbeam. Then comes the definition of the distance betweensuccessive rings i and i+ 1

∆rplanei = h(cot θi+1 − cot θi) (2)

where ∆rplanei is the interval of i th ring on the plane. Thendistance between rings ∆ri would decrease compared with∆rplanei when the beam is obstructed by an obstacle, thenfeature points such as curbs or others which denotes thesaltus step are detected by checking whether ∆ri is withina certain interval defined by ∆rplanei

Si = [αrplanei , βrplanei ) (3)

where α and β are used to adjust the range bounds with β> α.

2) Point cloud weighting: After eliminating the featureless points of the current observation, a weighting methodis carried out to enhance point features for occupancy gridmapping and grid based matching. Weight of each point isdetermined by

wi ∝ (1− ∆ri

∆rplanei

)zi (4)

where zi is the height of i th point. Points with only positiveweights were reserved. Results of point cloud weightingis shown in Figure 3. where colours of points denote theweight of each point. It is obviously that points within theroad surface or other horizontal flat planes are eliminated,meanwhile points fall on curbs, tree trunks or buildingfacades persist in the weighted point cloud.

It should be noted that the proposed method only suits forflat road surface or road with gentle slopes because of thepoint cloud weighting approach. Laser points irradiation toboth positive and negative obstacles as well as hollows onthe road, will be distributed high weights, which will impactthe occupancy grid forming process described as follows.

B. Occupancy grid map generation

Occupancy grid map could be formed easily with weightedpoint cloud. Algorithm 1 describes the generation procedure

−50 −40 −30 −20 −10 0 10 20 30 40 50

−30

−20

−10

0

10

20

30

40

X [meters]

Y [

me

ters

]

Weight

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fig. 3. Illustration of weighted point cloud, with color denotes weight ofeach point.

(a) (b)

Fig. 4. Occupancy grid map illustration with orthoimage of the scenario(a) and generated occupancy grid map (b).

in details. Local area is divided into cells with resolutionof 0.1 m, then points are projected on corresponding cells.Each cell with points fall in it is calculated a set of valueswhich describe the highest point, lowest point, and the sumof weight of the associated points. Then the value of each cellin the local occupancy grid is obtained through a empiricalfunction that takes both weight sum and height gap of the cellinto consideration. The purpose is to obtain a clear grid mapthat could describe the environmental features accurately andreduce the noisy points as much as possible. Cells with alower number of points under point threshold Tcell will beconsidered as totally free to avoid noises from obtaining thepoint cloud. Results of occupancy grid generation method isshown in Figure 4 with the corresponding orthophoto of thescenario, road curbs, tree trunks and building facades wereemphasized through the occupancy grid very clearly.

IV. LOCALIZATION

The extracted featured points are transformed to specificvalues that stored by the occupancy grid map to performlocalization. This global map is accessed by a map managermodule to provide a smaller local reference map for mapmatching, based localization method, therefore the compu-tational complexity could be decreased at the same time.

64

Algorithm 1: Convert weighted point cloud to occupancygrid map

Input: Weighted point cloudpw = {(p1, w1), (p2, w2), . . . , (pk, wk)},cell points threshold Tcell,occupancy grid size m*n

Output: Occupancy grid map Mfor i = 1; i ≤ k; do

determine corresponding grid cell cuv of pi;if zi > heightMaxuv then

heightMaxuv = ziendif zi < heightMinuv then

heightMinuv = zi;endweightSumuv+ = wi;

endfor i = 1; i ≤ m; do

for j = 1; j ≤ n; doif pointNumberij < Tcell then

continue;endheightGapij = heightMaxij − heightMinij ;Mij = function(heightGapij , weightSumij);

endendreturn M ;

Vehicle pose initialization is also executed with the localmap, the rough position was provided by the ordinary GPS.

A. Pose initialization

It is necessary for intelligent vehicle to locate with anunknown initial position, which is also known as the kid-napped robot problem. A brief idea of solving this problemis to obtain an pose estimation roughly and refines theresult iteratively. Therefore a pyramid map matching methodis used here to perform initial pose estimation. This partof work uses a similar strategy that presented in [5], butwith different map representations. The generated globaloccupancy grid map is recalculated in lower resolution toobtain a coarser grid map with larger scale covered.

As there are plenty of similar scenarios in the urbanenvironment, an onefold grid pyramid based initializationmethod would still result in negative outcomes and couldhardly recover from the false initialization, unless there is astrong constraint for pose correction. Therefore a low costGPS with the accuracy about 10 m is still required to givethe initial pose estimation for pyramid matching. The vehiclewill keep performing dead reckoning to generate local pointcloud before the pyramid matching module could achieve areliable result.

B. Localization with maximum likelihood matching

The general formulation of the localization problem isestimating the joint probability posterior of the robot pose

over the global map, all previous sensor observations andcommand inputs:

P (xt|Mt, z0:t,u0:t,x0) (5)

where xt is the pose of the vehicle at time t, Mt isthe reference map at time t, z0:t and u0:t stand for theobservations and motion measurements up to time t, andx0 is the initial pose of the vehicle.

The motion model should be discussed first before intro-ducing the localization method. Here only the 2D positionand orientation of the vehicle was considered. Vehicle stateat time t is a set of 2D position and orientation candidatesXt = {x1,t,x2,t, . . . ,xn,t}, and x = (x, y, θ). Because ofthe odometer and IMU, increment of mileage and orientationcould be easily obtained, then the vehicle state predictionmodel was described as follows,

xt = xt−1 + δst−1 cos(θt−1) (6)yt = yt−1 + δst−1sin(θt−1) (7)

θt = θt−1 + ωδT (8)

ω stands for the angular velocity of yaw, which is obtainedby IMU, δT is the time interval, δst−1 is the mileageincrement during time t and t − 1, which is obtained byodometer. Thus the vehicle’s motion is obtained.

The Maximum Likelihood Matching method is carriedout to perform measurement update according to the sensorobservation. Most recent observation zt is taken into con-sideration after being weighted, in order to refine the stateestimation provided by vehicle motion model:

x′t = arg max{P (zt|xt−1,Mt−1)× P (xt|xt−1,ut)} (9)

where P (xt|xt−1,ut) is the vehicle motion model thatprovides the list of candidate poses. P (zt|xt−1,Mt−1) isthe measurement model of the candidates corresponding tocurrent reference occupancy grid map of time t Mt.

The likelihood of the model is

P (zt|xt−1,Mt−1) ∝N∑i=1

P (mit−1,pw) (10)

where N is the number of cells contained in the referenceoccupancy grid while mi

t−1 stands for each cell. pw denotesthe weighted point cloud. P (mi

t−1,pw) is the likelihoodof weighted points fall into the corresponding grid cell.Thus vehicle pose estimation from measurement model isobtained, vehicle state is then updated with pose correctionfrom motion model prediction.

V. EXPERIMENTAL RESULTS

Experiments were carried out using datasets of urbanenvironment collected by our mobile testing platform Cyber-Tiggo. A Velodyne VLP-16 laser scanner was fixed on topof the vehicle, rear wheel encoder as well as Xsens MTxIMU were also equipped to provide odometry information.RTK-GPS was used to generate the global occupancy grid

65

(a) (b)

Fig. 5. Dataset 1 with a 12.5 km distance trajectory in urban environmentcontained numerous buildings. (a) Travelled route with colored trajectory (b)Comprasion between estimated vheicle path (blue) and ground truth (red).

X [meters]

Y [m

ete

rs]

0 200 400 600 800 1000 1200

100

200

300

400

500

600

700

800

900[m]

0

1000

2000

3000

4000

5000

(a)

0 200 400 600 800 1000 12000

200

400

600

800

1000

X [meters]

Y [

me

ters

]

Ground truth

Proposed method

(b)

Fig. 6. Dataset 2 with a 5.3 km distance trajectory in urban environmentcontained few buildings. (a) Travelled route with colored trajectory (b)Comprasion between estimated vheicle path (blue) and ground truth (red).

map for localization and provide the ground truth for resultevaluation. Algorithm was implemented using ROS frame-work and MRPT tool kit, all experiments were carried outon a laptop equipped with an Intel Core i7-2620M CPU atthe clock speed of 2.7 GHz. The localization method tookon average 20 ms to process an observation frame. Theproposed approach was tested with two large scale urbanscenarios with the mobile platform traveled at the speedranging from 25 km/h to 30 km/h. Datasets in ROS .bagformat on which the above experimental result were carriedout are also provided by us.

TABLE IDETAILS OF THE TWO DATASETS USED IN GLOBAL MAPPING AND

LOCALIZATION EXPERIMENTS

Dataset Length Map area Resolution Disk usage1 12.5 km 3000 m x 1500 m 0.1 m 45.2 Mb2 5.3 km 1000 m x 900m 0.1 m 19.5 Mb

Figure 5 and Figure 6 denotes the two datasets used for

(a)

−1 0 10

100

200

300

400

500

600

700

800

900Longitudinal deviation

[meters]

−1 0 10

200

400

600

800

1000

1200

1400Lateral deviation

[meters]

−2 −1 0 1 20

200

400

600

800

1000

1200Heading deviation

[degrees]

(b)

Fig. 8. Errors in longitudional, lateral and heading (a) Dataset 1 and (b)Dataset 2.

localization experiments respectively. Details of the datasetsare shown in table I with the total trajectory length, areasize, map resolution and disk usage. With the occupancy gridrepresentation of the environment, storage space as well asthe resource requirement of map file accessing operationswould be reduced effectively.

Figure 7 shows the localization results of each datasetin terms of translation, rotation, longitudinal and lateralerrors. Lateral translation error is restricted under a lowlevel, meanwhile the rotation error is also acceptable. Thereason of obtaining such performance under above itemsis the use of occupancy grid representation proposed inthis work that stores local features such as curbs, buildingfacades, et al. The strategy of maximum likelihood matchingwith weighted point cloud enhanced the lateral localizationperformance further. On the other hand, longitudinal erroras well as the overall translation error revealed a tendencyof increasing while traveling along road segmentations. Thatwas caused from lacking of enough longitudinal features toperform better matching result along the heading direction.But the trend of getting worse localization result would beterminated when longitudinal features show up, especiallywhen driving into an intersection, which is shown evidentlyin Figure 7 (upped and middle row, both columns). Resultsobtained from dataset 1, where there were many buildingswithin the scenario, provides a better overall performancethan results obtained using dataset 2, which also addressesthe issue between lateral and longitudinal features, sincethe scenario of dataset 2 contained only roads but withoutenough buildings.

TABLE IILOCALIZATION RESULTS IN MEAN VALUE AND STANDARD DEVIATION

Item Dataset 1 Dataset 2

Translation (m) Mean 0.3603 0.6108Std 0.2236 0.3279

Rotation(degree)

Mean 0.0587 0.0444Std 0.0065 0.0146

Longitudinal (m) Mean 0.2902 -0.4659Std 0.4746 0.5737

Lateral (m) Mean 0.0408 0.0048Std 0.1650 0.4848

Errors in longitudinal, lateral and heading at each iterationare illustrated in Figure 8. Results of dataset 1 show that mostof the errors in lateral are bounded by ± 0.3 m, while mostof heading errors are limited to ± 1◦ .

Table II shows the localization results in mean valuesand standard deviations. The proposed method could providea relatively accurate position within urban environmentscontain more surrounding information rather than roads onlyaccording to the statistical data.

VI. CONCLUSION AND FUTURE WORK

In this paper, we have presented a framework for gridbased localization in urban environment. The method wasdesigned for large scale urban environments with relativelylower sensor configuration. In order to describe the envi-ronment with specified features, model based point cloud

66

0 2000 4000 6000 8000 10000 120000

0.5

1

1.5Translation error

Traveled distance [meters]

Err

or

[mete

rs]

Proposed method

0 2000 4000 6000 8000 10000 12000

−1.5

−1

−0.5

0

0.5

1

1.5

Longitudinal/Lateral

Traveled distance [meters]

Err

or

[mete

rs]

Longitudinal error

Lateral error

0 2000 4000 6000 8000 10000 12000−3

−2

−1

0

1

2

3Heading Error

Traveled distance [meters]

Err

or

[degre

es]

Proposed method

0 1000 2000 3000 4000 5000 60000

0.5

1

1.5

2

Translation error

Traveled distance [meters]

Err

or

[mete

rs]

Proposed method

0 1000 2000 3000 4000 5000 6000

−1.5

−1

−0.5

0

0.5

1

1.5

Longitudinal/Lateral

Traveled distance [meters]

Err

or

[mete

rs]

Longitudinal error

Lateral error

0 1000 2000 3000 4000 5000 6000−2

−1

0

1

2Heading Error

Traveled distance [meters]

Err

or

[degre

es]

Proposed method

Fig. 7. Localization results obtained with dataset 2 (left column) and dataset 1 (right column). Upper row of both columns are positioning errors comparedwith ground truth. Middle row of both columns are positioning errors along trajectories, in the form of longitudinal (red line) and lateral error (blue line).Lower row of both columns are heading errors.

extraction as well as point cloud weighting methods werecarried out. Maximum likelihood matching method accordingto weighted points and grid map generated from them wasdeveloped. Results have shown that the proposed method iscapable of handling urban localization with a long trajectory.Meanwhile, with the map management module, localizationprocess within a large region was still efficient and accurate.

Though examined with long distance experiments andproved to be effective, the proposed method still need com-parison with existing methods such as point cloud matchingbased localization algorithms to verify the advantages anddisadvantages. Currently we are working towards improvingthe matching results, especially improving the Longitudinallocalization performance during long trajectories. IntegratingSLAM modules into the localization framework is also in theplan.

REFERENCES

[1] V. Trung-Dung, O. Aycard, and N. Appenrodt, “Online Localizationand Mapping with Moving Object Tracking in Dynamic OutdoorEnvironments,” IEEE Intelligent Vehicles Symposium, pp. 190–195,2007.

[2] N. Suganuma and T. Uozumi, “Precise position estimation of au-tonomous vehicle based on map-matching,” IEEE Intelligent VehiclesSymposium, Proceedings, no. Iv, pp. 296–301, 2011.

[3] I. Baldwin and P. Newman, “Laser-only road-vehicle localization withdual 2D push-broom LIDARS and 3D priors,” IEEE InternationalConference on Intelligent Robots and Systems, pp. 2490–2497, 2012.

[4] T. Suzuki, M. Kitamura, Y. Amano, and T. Hashizume, “6-DOF local-ization for a mobile robot using outdoor 3D voxel maps,” IEEE/RSJ2010 International Conference on Intelligent Robots and Systems,IROS 2010 - Conference Proceedings, pp. 5737–5743, 2010.

[5] J. Xie, F. Nashashibi, M. Parent, and O. Garcia-favrot, “A real-timerobust global localization for autonomous mobile robots in largeenvironments,” Control Automation Robotics & Vision (ICARCV),2010 11th International Conference on, pp. 1397–1402, 2010.

[6] J. Wiest, H. Deusch, D. Nuss, S. Reuter, M. Fritzsche, and K. Di-etmayer, “Localization based on region descriptors in grid maps,”IEEE Intelligent Vehicles Symposium, Proceedings, no. Iv, pp. 793–799, 2014.

[7] A. Y. Hata, F. S. Osorio, and D. F. Wolf, “Robust curb detection andvehicle localization in urban environments,” IEEE Intelligent VehiclesSymposium, Proceedings, no. Iv, pp. 1257–1262, 2014.

[8] A. Y. Hata and D. F. Wolf, “Feature Detection for Vehicle Localizationin Urban Environments Using a Multilayer LIDAR,” IEEE Transac-tions on Intelligent Transportation Systems, pp. 1–10, 2015.

[9] K. Jo, Y. Jo, J. K. Suhr, H. G. Jung, and M. Sunwoo, “PreciseLocalization of an Autonomous Car Based on Probabilistic NoiseModels of Road Surface Marker Features Using Multiple Cameras,”IEEE Transactions on Intelligent Transportation Systems, vol. 16,no. 6, pp. 3377–3392, 2015.

[10] A. Schlichting and C. Brenner, “Localization using automotive laserscanners and local pattern matching,” IEEE Intelligent Vehicles Sym-posium, Proceedings, no. Iv, pp. 414–419, 2014.

[11] K. Yoneda, C. Yang, S. Mita, T. Okuya, and K. Muto, “Urban roadlocalization by using multiple layer map matching and line segmentmatching,” IEEE Intelligent Vehicles Symposium, Proceedings, vol.2015-August, no. Iv, pp. 525–530, 2015.

[12] R. W. Wolcott and R. M. Eustice, “Visual localization within LIDARmaps for automated urban driving,” IEEE International Conference onIntelligent Robots and Systems, no. Iros, pp. 176–183, 2014.

[13] P. Ruchti, B. Steder, M. Ruhnke, and W. Burgard, “Localization onopenstreetmap data using a 3d laser scanner,” 2015 IEEE InternationalConference on Robotics and Automation (ICRA), pp. 5260–5265,2015.

[14] Z. Alsayed, G. Bresson, F. Nashashibi, and A. Verroust-Blondet, “Pml-slam: a solution for localization in large-scale urban environments,”PPNIV-IROS, 2015.

67