LiDAR Configuration Comparison for Urban Mapping System

Joowan Kim, Jinyong Jeong, Young-Sik Shin, Younggun Cho, Hyunchul Roh and Ayoung Kim1∗1Department of Civil and Environmental Engineering, KAIST, Republic of Korea

(Tel : +82-042-350-3672; E-mail: [jw kim, jjy0923, youngsik, yg.cho, rohs , ayoungk]@kaist.ac.kr)

Abstract—The Mobile Mapping System (MMS) is widely usedwhen mapping urban environment. The critical challenge formapping accuracy is at localization accuracy under highlysporadic global positioning system (GPS) signal. To tackle thisissue, approaches often rely on cameras and Light Detectionand Ranging (LiDAR)s to exploit visual and spatial features inthe environment. Among many sensors, this paper focuses onthe use of LiDAR, especially evaluating the LiDAR types andmechanical configurations. In this paper, we compare two typicalLiDAR configurations, push-broom (2D) and 360 scanning (3D)style, in terms of the resulting mapping performance. Resultingmaps from two configurations over the same place are directlycompared to evaluate characteristic of each LiDAR configuration.

Keywords—Mobile Mapping System, LiDAR, SLAM

1. INTRODUCTION

Many mobile mapping systems aim at constructing anaccurate map of urban environment by merging navigationaland perceptual sensors. Toward this objective, recent mobilemapping systems researches focus on a dense 3D maps fromLiDAR and visual recognition. Among many perceptual sen-sors, LiDARs are gaining popularity for many years for densemap generation and point cloud based localization.

Typical LiDAR sensors can be categorized as 2D and 3DLiDAR. The 2D LiDAR sensors are array of 1D LiDARs, andeach 2D scan produces a slice of point cloud data. These 2DLiDAR are often mounted perpendicular or horizontal to theground, so the relationship between the acquired point cloudand the vehicle coordinate system can be directly configured.For the 2D LiDAR based systems, it is more straightforwardto obtain high-level building information.

The 3D LiDAR rotates the array of 1D LiDARs constructing360◦ field of view. In the case of using 3D LiDAR, researchesfocus on information centering on the building adjacent tothe vehicle at the ground level. Since the point cloud iscontinuously updated even when the vehicle does not move, ithas an advantage when handling the occlusion in both staticand dynamic environment.

Each LiDAR configuration possesses its own advantagesand drawbacks. Using 2D LiDAR might be vulnerable tovacancies in occluded area when scanning a slice of mea-surement from the urban structure per measurement. The 3DLiDAR is beneficial being robust against occluded region butneeds to deal with large data and motion induced data incon-sistency. This paper addresses this difference and comparesthe two systems in terms of mapping performance.

2. RELATED WORKS

Similar types of urban mapping systems as ours havebeen introduced in the literature [1, 2, 3, 4]. Blanco et al.,

[1] developed similar mobile mapping system with verticallyinstalled 2D LiDARs. In their follow-up research, the 2DLiDAR was also used [2]. Pandey et al., [5] used vehicle withboth 2D and 3D LiDARs. Their vehicle was equipped with a3D LiDAR scanner (Velodyne HDL-64E) and two 2D push-broom forward-looking LiDARs (Riegl LMS-Q120). Modifiedplatform appeared in their follow up research [6]. Instead ofusing both 3D and 2D LiDARs, their vehicles were equippedwith four 3D LiDAR scanners (Velodyne HDL-32E). In theirwork, a prior map was generated by using the LiDAR. Thenthe vehicle was localized to this prior map using a monocularcamera based on mutual information (MI).

Similar to our works, system analysis has been reported.Petrie [7] analyzed several mobile mapping systems whichwere commercially available. In the paper, author presentedboth imaging and mapping performance of the vehicles, pro-viding detailed survey of widely used platforms. Street Mapper360 [8] developed a mapping module that is applicable tovarious mobile platform. The module is capable of producinga rich (e.g., 600,000 points/sec) point cloud even under motion,while providing a cm-level accuracy. Topcon IP-S3 HD1 [9]also provides mapping system with high-quality data. Theplatform maps the environment using a high-speed scanningmodule (e.g., 700,000 points/sec).

3. TWO URBAN MAPPING SYSTEMS

This paper compares aforementioned two LiDAR configu-rations in terms of map data density, obstructed area, and pointcloud richness. The presented two sensor systems commonlyinclude the configuration of the four cameras, inertial measure-ment unit (IMU), fiber optic gyro (FOG), conventional GPS,and the encoder system for vehicle odometry.

3.1. Push Broom 2D Scanning LiDAR

First type of mobile mapping system is as shown in 1(a).This system is a car-type vehicle with three 2D LiDARs,four RGB cameras and other navigational sensors. The vehiclepose is estimated by the IMU, Differential Global PositioningSystem (DGPS), and wheel encoders mounted on the vehicle.Attitude data is collected using IMU that runs in 100 Hz with0.1◦ accuracy. Rotation of each wheel is measured using wheelencoder (100 Hz). For data logging and processing, we usea desktop PC configured with Intel i7 CPU (3.4GHz), 8GBRAM and SSD storage. Two 2D LiDARs are pointing eachside of the vehicle with sweeping plane perpendicular to theground. By mounting the LiDARs in this configuration, weaccumulated a slice of data per measurement and achieveddata around the road while the vehicle was driving. Based

(a) Push broom type 2D LiDAR (b) Tilted 360 type 3D LiDAR

Dimensions 1.67 m × 1.36 m × 0.31 m (L × W × H )Dry weight 35.8 kg

Vehicle Hyundai Avante XD (model 2006)LiDAR SICK LMS291,200 (75Hz)

Imaging sensor PointGrey Flea3, 1380 × 1024 pixel,12-bit CCD (30Hz)

GPS HUACE B20 (1Hz)IMU sensor Xsens MTi (100Hz)

Wheel encoder Autonics E68S, rotary encoder type (100Hz)Processor Intel(R) Core(TM) i7-3790 CPU@3.4GHz

Battery Delkor 80 Ah, 12 V ,leadacid type

(c) Specifications of UMS sensors

Dimensions 2.39 m × 1.41 m × 0.35 m (L × W × H )Dry weight 19.7 kg

Vehicle Toyota Prius (model 2015)LiDAR Velodyne PUCK [VLP-16] (15Hz, 16channel)

Imaging sensor PointGrey GigE, 1380 × 1024 pixel,12-bit CCD (10Hz)

GPS U-Blox EVK-7P (1Hz)FOG KVH DSP-3000 (100Hz)

IMU sensor Xsens MTi-300 AHRS (100Hz)Wheel encoder Autonics E68S, rotary encoder type (100Hz)

Processor Intel(R) Core(TM) i7-6700 CPU@3.4GHzBattery Devicemall 100 Ah, 28 V , lithium-iron type

(d) Specifications of UMS sensors

Fig. 1. Sensor configuration for two mobile mapping systems. Below are the summaries of the specification.

on a recommendation [10], the vehicle speed was limitedto 15− 30 km/h. This vehicle speed limitation would bethe critical limitation for mapping when the fast mapping isrequired.

Encoder

IMU

FOG

GPS

VRS-GPS

Altimeter

2D LiDAR

Camera

Odometry

Client

(6D pose)

iSAM

(SLAM

back-end)

3D

World

model

Colored

Point cloud3D LiDAR

Fig. 2. Sensor data diagram for two urban mapping systems. The color of eachletter represents the sensor configuration of the two systems. Black indicatessensors the commonly used in both systems, and red and blue are the sensorsin the 2D LiDAR system and the 3D LiDAR system respectively.

3.2. Tilted 360 Scanning 3D LiDAR

Second type of the platform is equipped with two 3DLiDARs (Velodyne VLP-16) as shown in Fig. 1(b). Similarto the 2D LiDAR, the detailed sensor configuration is shownin the table (Fig. 1(d)). Two tilted LiDARs, four cameras,wheel encoders and GPS are mounted outside of the vehicle.Wheel encoders and IMU are the same specifications aspreviously mentioned systems with 2D LiDARs. The tiltedmount for the two LiDARs minimizes laser shadowing region.The advantage of using this LiDAR configuration is that eachLiDAR compensates another’s occluded area. Through post-processing, angle, range, and intensity data are fused togetherfrom both LiDARs to create a more complete and accurate 3Dmap. The interior of the vehicle is equipped with FOG, IMUand wheel encoder modules. All of these sensors are processedby a desktop PC configured with Intel(R) Core(TM) i7-6700CPU@3.4GHz installed inside the vehicle.

3.3. Sensor System Software

For both sensor configurations, our software architecture isas shown in Fig. 2. The 6-degree of freedom (DOF) vehiclepose is calculated using navigational sensors. Using thesecomputed poses, colored point cloud is obtained by fusingLiDAR and camera images. As the simultaneous localizationand mapping (SLAM) back-end, incremental smoothing andmapping (iSAM) is used to optimize entire vehicle trajectory.

(a) The aerial map (b) Resulting map (2D LiDAR) (c) Resulting map (3D LiDAR)

(d) The aerial map (e) Resulting map (2D LiDAR) (f) Resulting map (3D LiDAR)

Fig. 3. Two sample maps obtained from each mobile mapping system

Accurate 3D world model is reconstructed with respect to thecomputed vehicle trajectory. The more detailed explanationon SLAM application reaches beyond the scope of this paper,and we refer readers to our previous works on map generationusing the aforementioned systems [11, 12, 13].

4. TWO SYSTEM ANALYSIS

In this section, we present the comparison results fromtwo different LiDAR configurations described earlier. Twoexperiments over the same environment were performed byusing each sensor system. For both tests, we used synchro-nized cameras, IMU, wheel encoders and Light Detection andRanging (LiDAR)s. Using SLAM based approach [13], wegenerated 3D map of the environment as shown in Fig. 3.

4.1. Data Density

As shown in Table. 1, overlapping points in the case of atilted 360 scanning system are significantly greater than in thepush broom style mapping system. The 2D LiDAR system hasa 180◦ field of view (FOV) and sensing range is 80 m. On theother hand, the 3D LiDAR of the tilted system has a 360◦ FOVand sensing range is 100 m. The 3D LiDAR system allowsmore vertical objects and wall scanning along the vehicle’sdirection of travel, and hence a richer distribution of points

TABLE 1SUMMARY OF VOXEL CONVERSION RESULT FOR PUSH BROOM

SCANNING SYSTEM

2D LiDAR 3D LiDARPoint cloud 3,495,661 8,434,863Data rate 125,000 Points / Sec 300,000 Points / Sec

for buildings and trees. Blue box in Fig. 3(a) and Fig. 3(d)shows substantial density difference in the two configurations.

However, the cost is at the data size. The 2D LiDAR config-uration collects measurement data of the defined scan range75 times per second (361 measurement data per scan). The3D LiDAR, on the other hand, takes 1.33 ms to accumulateone data packet [14]. This implies a data rate of 754 datapackets per second. In particular, we can see that the number ofpoints is about three times more than 2D LiDAR system whenanalyzing buildings and trees in the blue box. Our comparisonshows that the appearance of the object is richer with the 3DLiDAR while larger data packet size is required.

4.2. Blind Region

As described in the §3.2, The 3D LiDAR based mappingsystem was mounted on the vehicle at an angle of about 45◦.Therefore, the system avoids occlusion by any close objects(e.g., nearby buildings and cars), enabling the system to have asmaller blind region. The red squares in Fig. 3(a) and Fig. 3(d)show these blind regions caused by occlusion. Correspondingpoint cloud regions are empty when mapped by 2D LiDAR(Fig. 3(b) and Fig. 3(e)). On contrary, the point cloud is farricher when using 3D LiDAR.

5. CONCLUSION

This paper reported comparison between two typical LiDARconfigurations which were widely used in the MMS. Forpush broom scanning system with 2D LiDARs, the LiDARswere with a single channel and a relatively low FOV. Thisconfiguration provided direct and easy implementation but waslimited when creating a dense map with low vehicle speed.Another mapping system was equipped with 3D LiDAR that

has 16 channels with 360◦ scanning ability. The comparisonreported from real experiments showed that the blind area canbe minimized through the tilted 3D LiDAR configuration butwith larger data packet required.

ACKNOWLEDGMENT

This work was supported by [SLAM-based Lane Map Gen-eration for Complex Urban Environment] project funded byNaverLabs Corporation. This material is also based upon worksupported by the MOTIE, Korea under Industrial TechnologyInnovation Program (No. 10067202 and No. 10051867). J.Kim and J. Jeong were supported by MOLIT, Korea via U-City Master and Doctor Course Grant Program.

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