The 19th Korea -Japan Joint Workshop on Frontiers of Computer Vision

Traversable Ground Detection Based OnGeometric-Featured Voxel Map

Bo Gil Seo1, and Myung Jin Chung

Robotics Research Laboratory,Department of Electrical Engineering, KAIST

Daejeon, Republic of Koreabogilsmt@rr.kaist.ac.kr, mjchung@ee.kaist.ac.kr

Abstract-The process of finding traversable ground isimportant to autonomous vehicles. To achieve this, 3D maps thatcontain the information about vehicle's surroundings aregenerally used. One of the simple methods to represent 3D mapsis to use 3D point clouds collected by range sensors. But, itrequires lots of memory and computation time to deal with pointclouds. To solve these problems, we propose a novel traversableground detection method using a 3D mapping algorithm. Theproposed mapping algorithm includes plentiful geometricinformation based on voxels, which is called Geometric­Featured Voxel (GFV) maps. In our experiments, point cloudscollected in urban environments are tested to evaluateperformance of the proposed algorithm.

Keywords: 3D mapping, Voxel, Ground Detection, Range Sensor,Geometric Feature

I. INTRODUCTION

Representing vehicle's surroundings on 3D map is anessential step for autonomous vehicles. For making a 3D map,autonomous vehicles use range sensors such as laser scannersto recognize their surroundings. The range sensors are used tocollect range information of surroundings [1], [2]. Thesesensors provide range data in the form of 3D point clouds.But, it requires lots of memory and computation time to dealwith point clouds.

To overcome the difficulties as mentioned, 3D mappingtechniques have been studied in the field of robotics. NormalDistribution Transform (NDT) is a method to representgeometric information using mean and covariance [3]. Meanand covariance of geometric distribution of point clouds arestored in each 2D or 3D NDT grids. This method has beenused for path planning [4]. But in previous works of NDT,geometric information of NDT is not various. Anotherapproaches used for dense data like point clouds is a voxelgrid map [5]. Voxel grid map is a simple method to represent3D maps. It does not require much memory, so computationtime can be reduced. However, to represent objects in detail,the size of unit voxel must be small. It requires memory asmuch as voxel density. On the contrary, if the size of unitvoxel is too large, the quantization error of voxel also

becomes large. Moreover, voxel contains only spatialinformation not geometric information.

In our previous work [8], we suggested a 3D mappingalgorithm including plentiful geometric information based onvoxels, which is called Geometric-Featured Voxel (GFV)maps. By analyzing the distribution properties of point cloudsof each voxels, the GFV maps can represent vehicle'ssurroundings geometrically. This paper extends our previouswork to detect traversable grounds. By using geometricinformation of GFV maps and comparing with the heightinformation of each voxels, we can segment the GFV mapsinto ground voxels and non-ground voxels effectively. Ourproposed method is generally adaptable to detect groundregardless of surface types of grounds. Also it helps tounderstand the urban environments because the processedvoxels do not lose their geometric properties.

This paper is organized as follows. We present the proposedmethod in the Section II, experiments and result in theSection III, and conclusion in the Section 4.

II. The Proposed Method

A. Geometric-Featured Voxel

Geometric-Featured Voxel consists of data structuresstoring feature values in each grid instead of spatialoccupancy. Previously, saliency features [9] are well knownbased on mean and covariance that represent characteristicsof 3D objects. They can be used to distinguish the 3D pointclouds by their geometric forms. Using the covariance matrixof 3D points in a local neighborhood, the distribution of 3Dpoints is estimated by principal component analysis. Thecovariance matrix is decomposed into principal componentsordered by increasing the eigenvalues AI' A2 , A3 and thecorresponding eigenvectors el , e2 , e3 , where Al ~ A2 ~ A3 •

But saliency features are not plentiful to distinguish thevarious objects and also are not adaptable in urbanenvironments. In our previous work, we proposed enhancedgeometric features which are suitable for urban structures.They make the features more separable for distinguishing theurban objects. Table I denotes the feature names and itsdistinguishable objects.

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The 19th Korea -Japan Joint Workshop on Frontiers of Computer Vision

TABLE IENHANCED GEOMETRIC FEATURES

Name Symbol Distinguishable object

Point-ness fpoint Tree

Horizontal Surface-ness fh-surf Road

Vertical Surface-ness fv-surf Wall

Post-ness fpos t Post

The feature values are determined by bottom formulations.

B. Ground Extraction

Require: Geometric-Featured Voxels V

1: Set the threshold of height: L

2: for all feature voxels Vi

3: if Vi has horizontal surface feature

4: for all horizontal surface feature voxels V.]

5: if x, y values of Vi' Vi are same and h < L then

6: Vi = Vground

7: end if8: end for9: else then

10: for all horizontal surface feature voxels V.]

11: if x, y values of Vi , Vi are same and h > L then

12: Vi = Vnon-ground

13: end if14: end for15: end if16: end for

Algorithm 1 Extract Traversable Ground

Fig. 2. The horizontal parts of building are seen like blue color in GFVmap. This fact makes possibilities for autonomous vehicle tomisunderstand the traversable region.

also many other objects having a horizontal distribution inreality. In Figure 2, the building is partially seen like bluecolor because the horizontal parts exist in the building. If weonly determine that the region having a high value ofhorizontal feature in GFV is traversable, the autonomousvehicle may mistake the region which is unable to go for thetraversable region. Therefore, we propose a method to extractthe traversable region more extract rather than using GFVmap only. The procedure of extracting the traversable groundis listed in Algorithm 1.

(1)

I fpoint

1

f_I f h- sur f

-I1 fv-surf

Lfpost

I CpointA3

1 1- -I Ch_surf(Aj-A3)(A2 -A3 ) e3 zl

= IC,"",,(A] - A,)(A, - A,)II~ diag (l,l,O)111

1 1- -Lcpost (Aj - A2A3 ) ej zl

where the Cpoint' Ch-surf' C v- sur f and Cpost are normalizing constantsmaking the range of feature values from zero to one. Figure 1shows how to estimate the feature values according to theforms of point distributions shortly and visually.

To extract the grounds, some threshold is applied to mapaccording to the height value of each points [6]. But in thecase of using a threshold, if points located on the ground existunder the threshold, they are likely to be extracted as ground.And this method can't apply to the environment of whichground is steep. Also the method, which compares the meanwith neighbors on elevation map, is introduced [7]. In thecase of using mean with neighbors, if neighbors are notobserved due to sparse sensing, this method shows poorperformance.

Using the GFV map, we can detect the traversable groundeasily and intuitively. In a GFV map, the ground tends to beseen near the blue color due to the fact that distribution ofground is similar to horizontal. But, there are not only ground

Fig. 1. The geometric form of distribution of 3D points in eachvoxels determines the feature values and types of objects.

First, the voxels having the high horizontal surface featurevalues relatively are selected the candidates of groundbecause the distribution of points of ground is generallyhorizontal. That is the reason why horizontal surface voxelsare considered. Second, we select two voxels (Vi' Vi) whose

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The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision

x, y positions are same each other and compare the heightvalues between two voxels. The reason why we are able toaccept that idea is that some voxels are the base of othervoxels from the ground due to influence of gravity [10].Therefore, if the difference in height h between two voxelsis less than a predefined value L , they are designated as aground. This method guarantees that it is definitely workingin the environment of which ground is steep due to not usingany fixed height value and it does not depend on the data type,whether sequential data or not, due to searching all candidatesof the ground.

TABLE IISPECIFICAnON OF POINT CLOUD DATA

Section Number of Capacity Range (W x D x H)Name PointsKAIST 275,674 16,575KB IIOmx 45mx 23m

III. EXPERIMENTS AND RESULTS

A. Experimental Setup

Fig. 4. The experimental environment (KAIST)

Fig. 3. The sensor system, which is equipped on the vehicle (NewXe) ofRobot Research Laboratory, KAIST

local point clouds. The building tends to appear strongly byred color due to the tendency of vertical distribution of pointclouds in building. The road tends to appear strongly by bluecolor due to the horizontal distribution of point clouds in road.The tree tends to appear strongly by green color due to well­distributed point clouds in tree. Table III indicates aspecification of GFV map. As shown in Table III, thecompression rate of GFV is considerably high so that it doesnot require lots of memory.

TABLE IIISPECIFICAnON OF GEOMETRIC-FEATURED VOXEL MAP

TABLE IVPRECISION OF GROUND CLASS OF

GFV MAP AND PROPOSED METHOD

Section Number of Capacity Compression RateName VoxelsKAIST 58,574 3,459KB 79.13%

99.78%Proposed method

98.84%GFVmap

In Figure 7, the result of extracting traversable groundusing preprocessed GFV map is illustrated. The traversableground appears by light green and the non-ground appears bydark red. The precision of ground class of GFV map andproposed method is shown in Table IV. As you can see, theproposed method is more adaptable to detect the traversableground than GFV map despite of existing various objects incomplex urban environments. This fact indicates that ourproposed method is adapted well in urban environments.

For generating a GFV map, we build our sensor systemshown in Figure 3. The vehicle used in experiment is NewXe.As shown in Figure 3, there are two cameras (Point GreyFlea3) and one laser scanner (SICK LMS 291) in front of thevehicle. Likewise, there are pairs of camera (Point Grey Flea3)and laser scanner (SICK LMS 291) on each left and right side.On the top of the vehicle, the IMU (Inertial MeasurementUnits) sensor and DGPS (Differential Global PositioningSystem) sensor are equipped for estimating the vehicle pose.The vehicle mounting the various sensors gets the plentifulinformation needed to generate the 3D point clouds by online.

B. Result

For evaluating the performance of proposed method, weselect a KAIST data consisting of 3D point cloudsreconstructed based on the experimental environment shownin Figure 4. To evaluate the performance of algorithm, thedata containing various objects which are suitable to representby geometric features in urban environments are selected.The detailed specification of point clouds is described inTable II.

The GFV map is generated based on the point clouds ofKAIST as input data shown in Figure 5. The result of thegenerated GFV map is shown in Figure 6. As you can see, theobjects in GFV map are well distinguished by distribution of

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The 19th Korea -Japan Joint Workshop on Frontiers of Computer Vision

Fig. 5. The point clouds ofKAIST are used as input data to generate Geometric-Featured Voxel map.

Fig. 6. The result of Geometric-Featured Voxel map in KAIST (Green indicates Point-ness, yellowindicates Post, blue indicates Horizontal, red indicates Vertical and black indicates others)

Fig. 7. The result of extracting traversable ground using Geometric-Featured Voxel map. (Light greenindicates the traversable ground and dark red indicates non-ground)

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The 19th Korea -Japan Joint Workshop on Frontiers of Computer Vision

IV. CONCLUSION

In this paper, we propose a novel traversable grounddetection method using the proposed Geometric-FeaturedVoxel map. The proposed method provides plentifulinformation of urban structures and terrain and it gives apossibility to represent urban environments in 3D mapseffectively. Also, it is expected to be able to adapt on thevarious urban environments regardless of type of ground.This research is not only expected to extend the segmentationand classification of urban structure but in the field of robotnavigation.

ACKNOWLEDGMENT

This research was supported by the MKE(The Ministry ofKnowledge Economy), Korea, under the Human ResourcesDevelopment Program for Convergence Robot Specialistssupport program supervised by the NIPA(National ITIndustry Promotion Agency) (NIPA-2012-HI502-12-1002)

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