Journal of Mechanical Science and Technology 28 (11) (2014) 4381~4388 www.springerlink.com/content/1738-494x
DOI 10.1007/s12206-014-1005-6
Object tracking with enhanced data association using a 3D range sensor for an unmanned ground vehicle† Kuk Cho1, SeungHo Baeg2 and Sangdeok Park2,* 1
Intelligent Robot Engineering, University of Science and Technology (UST), Deajeon, 305-350, Korea 2 Department of applied robot technology, KITECH, Ansan, 426-910, Korea (Manuscript Received January 1, 2014; Revised July 16, 2014; Accepted September 1, 2014)
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Abstract This paper describes a novel object tracking method which combines a conventional global nearest-neighbor (GNN) method with an enhanced data association approach using 3D data features for an unmanned ground vehicle. If an object crosses in front of an observation system which has a measurement sensor equipped on a vehicle, a conventional distance-based object tracking mechanism encounters a data association problem owing to high uncertainties and a leak of information: correspondence between observations and tracks. To overcome these correspondence problems, we use the object’s 3D information such as statistical and geometrical information. In this paper, we describe a clustering and segmentation processes in conjunction with a distance-based nearest-neighbor approach, RBNN-2, and applied GNN with enhanced data association using both a kinematic and geometric models derived from 3D range data. To evaluate our method using a 3D LADAR sensor, a Velodyne HDL-32e, installed on the vehicle, we conducted an experiment involving two crossing humans and a ground vehicle to identify different human trajectories for a leader-following system. Keywords: 3D LADAR; Object tracking; Data association ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1. Introduction There are many important environment recognition techniques including stationary and moving objects. A wide variety of object tracking techniques have been studied in the numerous applications of missiles, airplanes, vehicles, pedestrians. The need for analysis pertaining to unmanned vehicle systems is increasing. In particular, multiple human detection and tracking technologies are crucial and primary when humans and unmanned ground vehicles are in close proximity to operation field such as a leader-following system, a swarm application, and urban driving. Most surveillance systems are capable of watching multiple objects. This ability is one of the most important performances for outdoor applications. A big issue for these tracking techniques is how to solve data association problems so that the observations are correctly assigned to the existing time sequential tracks. There are three main approaches of data association techniques. The global nearest-neighbor (GNN) algorithm finds the best assignment of observations to existing track using to the closest measurements [1]. The joint probability data association (JPDA) algorithm assigns observation*
Corresponding author. Tel.: +82 31 8040 6341, Fax.: +82 31 8040 6310 E-mail address:
[email protected] † This paper was presented at the ISR-2013, KINTEX, Seoul, Korea, October 24-26, 2013. Recommended by Guest Editor Byung Kyu Kim © KSME & Springer 2014
to-track with uncertainty derived from weighted sum of all observations in its gate [1]. The multiple hypothesis tracking (MHT) algorithm is a deferred decision logic in which alternative data association hypothesis are formed. It has a variable number of candidate tracks to find the best observation-totrack association [2]. Currently, laser detection and ranging (LADAR) systems are commonly used to recognize surrounding objects for an unmanned ground vehicle. The range sensors are widely used to obtain environment information to detect and track surrounding objects. Arras et al. and Premebida et al. detected people in 2D range data in a cluttered office environment and in a road environment using a single LRF sensor and a multilayer LADAR sensor, respectively [3, 4]. A shortcoming of 2D LRF approaches is that the sensor cannot provide enough information for object description. Recently, the developed 3D LADAR sensor provides a wealth of 3D point cloud data and extends the various sensor applications. 3D acquisition data has more wide information than acquisition 2D data. NavarroSerment et al. [5], Spinello et al. [6], and Kidono et al. [7] upgraded and expanded 2D point cloud approaches to a 3D point cloud. They subdivided the 3D point cloud of a target into several 2D layers and presented a center of tracking people from 3D range data using 3D LADAR. The data association is managed in two representative cases.
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The first case is that a close object blocks up a far object from the perspective of an observation sensor. The second case is that two objects are too close to distinguish using the clustering and segment technique. These commonly happen in actual situations such as invisible objects and miss-segmentation. General tracking methods depend strongly on the motion and appearance models [8-10]. If an object is not observable for a long time, a tracking mechanism will be absent. There are several approaches to overcome these problems using statistical or/and geometric information [10-12]. Petrovskaya and Thrun [13] approaches used both dynamic and geometric models of tracked vehicles and estimated the vehicle states using a single Bayes filter. They build efficient 2D representations out of 3D range data and improve the detection of poorly visible background vehicles. Kalyan et al. [14] used a highly accurate 3D LADAR system to segment the surrounding scene into regions of interest or blobs and the state of the pedestrians are estimated. The pedestrians are then tracked using a probability hypothesis density filter which is based on a random finite set theoretic framework. The filter is implemented using a Gaussian mixture technique. Azim et al. [15] developed a method in which detected moving objects are classified and tracked using a global nearest-neighbor technique. The method addresses both the problem of simultaneous localization and mapping and the problem of the detection and tracking of moving objects using 3D point cloud data from a commercialized Velodyne HDL-64e LADAR sensor. Zhang et al. [16] proposed a combination of a multiple hypothesis tracking algorithm and DPCR (dynamic point cloud registration) for the multiple vehicle-like target tracking in an urban environment. The tracking algorithm helps to improve the algorithm’s performance by discriminating and removing the dynamic objects on the surrounding environment scan. The traditional camera method is too sensitive on robust environments. The 2D LRF approach also has the problem of not using enough point data. There are numerous limitations when applied to many types of objects. Therefore, it is influenced by many previous limited problems such as partial object detection, occluded object tracking [8-10]. In accordance with the development of 3D LADAR sensor, many 3D points can be provided depending on the development of sensor technology. The 3D acquisition data has more representation information than the 2D data. However, management using 3D point cloud also has a considerable problem in the partial and full occlusion cases. Our research work reported in this paper differs from those reviewed above in some respects. This paper describes a novel object tracking approach that involves a combination of a conventional global nearestneighbor (GNN) tracking method and an enhanced data association method based on 3D data processing. The proposed method relies on a Velodyne 3D LADAR sensor HDL-32e installed on an unmanned ground vehicle. We combined the classical GNN approach with kinematic and geometric models derived from a 3D range sensor. This approach offers enhancements that allow the creation of the track with explicit
Fig. 1. Overview of object tracking using conventional GNN and enhanced data association with a kinematic model and a geometric model using a 3D range sensor for an unmanned ground vehicle.
data associations. The contributions of this study are as follows: i) the incorporation of an Kalman-filter-based GNN algorithm and a 3D LADAR system for object track estimations, and ii) the incorporation of a GNN algorithm and external kinematic and geometric models using 3D point cloud data from an UGV. We propose a 3D processing algorithm for enhanced object tracking. The rest of the paper is organized as follows. In Sec. 2, object detection for 3D LADAR is briefly explained with both the clustering segmentation methods from previous researches of the authors. Next, the moving object tracking using the GNN approach is presented with enhanced data associations and both kinematic and geometric models. Finally, we demonstrate an experimental application in the form of a leader-following system using an ATV (all-terrain vehicle) equipped a 3D LADAR sensor and basic navigation system for the detection and tracking of multiple human objects.
2. System configuration The section consists of a preprocessing (clustering and segmentation) with a KF-based GNN processing step and an enhanced data association step using both kinematic and geometric models as shown in Fig. 1. The object detection part consists of measurement to segment represented to stationary and moving objects. The object tracking part is composed of motion model update, optimal assignment, update track, and track management about track creation and deletion. 2.1 Object segmentation This section describes the object detection step, which consists of an object clustering step and an object segmentation step, referring to the previous works by the authors [12, 17]. First, the object clustering step is introduced. Given N the total number of point cloud data for one measurement frame, let the N number of the point cloud data pi = { xi , yi , zi , hi , vi , ri } , i = 1, …, N. The nearest neighborhood clustering approach in this research is RBNN-2 (radially bounded nearest-neighbor). It
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(a)
(b)
Fig. 3. The base coordinate frame and the vehicle coordinate frame with the position of a vehicle (triangle mark) and observations (red point) and moving object track (blue point and blue line).
Fig. 2. The object clustering step: (a) new cluster creation; (b) merge with neighbor points and clusters.
2.2 Multiple object tracking shows two processing steps of the clustering for making all points, not including a cluster, to valid points into clusters: a cluster creation step to create a new cluster as shown in Fig. 2(a) and a merge step to merge the point with nearestneighbor points and including cluster points as shown in Fig. 2(b). The first step for cluster creation is working with no clustering points { pi +1 , pi + 2 } which is in certain Euclidean distance from the pi -centered radius point as shown Fig. 2(a)-left. Fig. 2(a)-right shows grey points and grey color circle clustering is creating the new cluster Lm . The second step is a merging step when points or existing clusters in center Euclidean distance exist from the pi -centered radius point as shown Fig. 2(b)-left. Fig. 2(b)-right shows grey points and grey color circle clustering merged with neighbor points and a cluster. We need two parameters to perform the above process: the radius of the neighbor point r and the number of neighbor points in radius N r . As the explained in the clustering method, the 3D object clustering is used to finally generate a segmented object from all measurement points. In addition, the use of the number of the neighbor points has the effect of removing the minimum existing neighbor point number in the radius, min { N r } , and it has the effect of removing the noise data. For example, the number of neighbor points N r in Fig. 2(a) is 2, N r = 2 , and the number of the neighbor points in Fig. 2(b) is 5, N r = 5 . The clustered point cloud data are segmented using the following parameters of the object criteria, in this case the minimum point number to an segment, min {Ps } . After repeating these processes to all points which is a non-included cluster, we can get finally a clustered set, which are candidate segments. The object segmentation step comes next. A segment represents an object. It involves several criteria for the object detection result, in this case the minimum number of points of an object, the minimum size of an object and the minimum layer of an object min{Ps } .
This section describes a KF-based moving object tracking method using GNN and its track management with a basic single KF tracking process. And data association will be followed. 2.2.1 Object tracking model The Kalman filter is the most widely used state estimation algorithm. The filter consists of two steps: time update step and measurement update step. Let xb , k = [ xb , k yb , k x&b , k y& b , k ]T define a vehicle position on the base coordinate frame for the KF state vector at time step k, where xb , yb , x&b , y& b are the 2D object position and velocity on the base 2D coordinate of an object. It depicts with a triangle mark in Fig. 3. In the time update step, the state and error covariance are estimated from the previous time step k-1 to the current time step k. The object tracking system model is as follows:
xb , k
é xb , k -1 + x&b , k -1Dt ù ê ú yb , k -1 + y& b , k -1Dt ú =ê + wk ê ú x&b , k -1 ê ú y& b , k -1 ëê ûú
(1)
where Dt is the interval time and wk is the process noise at time step k which is normally distributed with mean zero and covariance Q . The measurement update step corrects the state and error covariance estimates with the measurement at time step k. The measurement update equation is as follows: é zb , x ù é x + xvi cosqb - yvi sin qb ù z b,k = ê ú = ê b ú + vk i i ë zb , y û ë yb + xv sin qb + yv cosqb û
(2)
where qb is the vehicle heading on the base coordinate. xiv ( xvi , yvi ) is the object 2D position of the measurement i-th
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object on the vehicle coordinate. z b ( zb , x , zb , y ) is also the observed 2D position on the base coordinate. vk is the measurement noise which is normally distributed with mean zero and covariance R . A single object process of KF predictor and corrector is as: xˆ -k = Axˆ k -1 Pk- = APk -1AT + Q K k = Pk- HT (HPk- HT + R ) -1 k
S j = HP j-1HT + R
Pk = (I - K k H )Pk-
(5)
L ijm = (z ib - Hxˆ -j )T S j (z ib - Hxˆ -j ) ,
where A and H are, respectively, state transition matrix and measurement matrix and defined as: 0 Dt 1 0 0 1 0 0
2.3.1 Kinematic data association The validation gate is a statistical score for corresponding of observation-to-track [18]. Let P j be the predicted track covariance of the i-th observation on the base coordinate frame and z ib be the state of the i-th observation on the base coordinate frame. The norm of the residual vector can be by mahalanobis metric:
(3)
k
xˆ k = xˆ + K k (z k - Hxˆ )
é1 ê 0 A=ê ê0 ê ëê0
2.3 Enhanced data association with model
0ù ú Dt ú é1 0 0 0 ù , H=ê ú. 0ú ë0 1 0 0 û ú 1 ûú
(4)
We define a set of trajectories contained in the estimated object’s history and let define an i-th track which has x1 ,K, x N . And NT is the number of the creation track. The measurement vector is z k = {xbi , ybi | i = 1,...., N M }T . N M is the number of the observation for one frame. The number of the state system represents the number of the track. In our experiment, the heading of the vehicle is obtained from an external measurement sensor to estimate direction, such as INS. And we should find that the optimal assignment of observation corresponds to tracks. T
2.2.2 Track management In moving object tracking, tracks are managed by allowing track creation, track update and track deletion. The performance of object detection is basically influenced by the object tracking. The optimal assignment finds all observed object to correspondence to existing track. In the creation and destroy steps, all of the new observed object is created. If the object doesn’t have any correspondence during several continuous steps, the list of tracking is eliminated. The system recognizes the relationship of the object’s position, which is calculated on the assignment process in the tracking step. As an object tracking process shown in Fig. 3, an object track is deleted with no-measurement during certain time. When a new candidate object, not correlated with any existing track, is obtained, a new track is created, which means that the size of a KF state increases. If it subsequently appears, it has a confidence of a new track of an object. It is a track create step. If an observed object is associated with tracks, we perform KF correct step with measurement. To manage tracks, we delete the track when there is any assignment track with observation during certain times, sequential exposure.
where i = 1,..., N i , j = 1,..., M j . N and M are, respectively, the number of tracks and the number of observation, measured objects. N i and M j are the i-th existing track and a j-th measurement object. It is the assignment problem and D ij is the matrix of score of i-th observation and j-th track. The threshold gating G can be defined for associating the observation and the track. An observation correspondence is established when L ijm , k £ G . We use threshold strategy for assignment. The system model is one of ways to prevent missing an object. Each existing track and observation consists of 2D coordinate, x-y axis. Dij (k) depicts the assignment score, which shows the existing track and measurement relationship. It works well with a complete motion model. However, the occlusion time is irregular and unrespectable. So, more information is needed to make an object tracking system. 2.3.2 Geometric data association Given each extracted j-th segment s j , we compute a set of a geometric model [3, 6]. All parameters are represented as Gaussian normal distribution with mean and covariance with normalizer h .
Lg =
{
1
h1
}
f1 (s j ) + h12 f 2 (s j )... + h18 f8 (s j )
where ㆍ Number of points: cardinality of sij denoted as n, f1 (s j ) =n. ㆍ Sphericity: this feature captures the level of sphericity from the ratio of the eigenvalue l1 , l2 , l3 extracted from the scatter matrix computed on P. f 2 (s j ) = 3 l3 å i li where l1 > l2 > l3 . ㆍ Flatness: the degree of planarity. f 3 (s j ) = 2(l2 - l3 ) å i li ㆍ Linearity: the level of linearity f 4 (sij ) = (l1 - l2 ) å i li ㆍ Standard deviation w.r.t centroid: the measure of compactness. f 5 (s j ) =
1 n -1
å (X i
i
- X) 2 , X is the centroid.
ㆍ Kurtosis w.r.t centroid: the fourth centralized moment of the data distribution in s j . f 6 (s j ) = å i (xi - x ) 4 / f 4 (s j ) .
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ㆍ Average deviation from median: an alternative measure of compactness. f 7 (s j ) = (1 / n)å i xi - %x , %x is the ) vector of independent medians %x = ( x%, y , %z ) . ㆍ Normalized residual planarity: a measure of flatness. n f8 (s j ) = å i (axi + byi + czi + d ) 2 where a, b, c, d are the parameters of the plane derived from the eigenvalues of the scatter matrix. We find the normalizer with predefined zero-mean dataset. The geometric data association is the accumulated update during certain steps with serialized data arrangement computing. L ijg is the geometric score of observation i-th and j-th track. It is derived from the difference of the geometric information of an observation and the geometric information of an existing track.
Fig. 4. The experimental vehicle and equipment with sensor configuration for observation and positioning.
2.3.3 Data association The entire data association proceeds with two decision tree steps: a kinematic model and a geometric model. From the previous section, L ijm and L ijg are the index i-th observation and j-th track of the kinematic model and the geometric model. First, the score of the model is the first index to make a decision. Second, if the index L ijm is less than threshold G and includes more than two objects, it means they are close to a kinematic model and hard to make a decision with the kinematic model. With the score of a geometric model L ijg , we can finally use the best value.
(a)
3. Experimental results The proposed combination of an object tracking method using 3D information with a GNN method and an enhanced data association approach is evaluated. In the experiment, 3D data using a LADAR sensor installed on an ATV (all-terrain vehicle) provides people switching in front of the vehicle. When an occlusion occurs in 3D LADAR data, it makes fault segmentation, such as two people are detected as one person and then missing track occurs. We applied distance-based clustering and segmentation method, RBNN-2, and it additionally leads to the effectiveness of a noise filtering. In the segmentation step, the radius of the neighbor point, the minimum of the nearest-neighbor point number in certain range and the minimum point number of one object are, respectively, 30 cm, 4 points and 20 points. All of moving ATV distance is about 200 m. And the minimum layer number requires more than 2. As shown Fig. 4, a number of sensors for autonomous driving are equipped on the vehicle as a 3D LADAR sensor (HDL-32e, Velodyne Inc.), a camera sensor (GC650C, Procillica Inc.), a GPS (SPAN-SE, Novatel Inc.), an IMU (HG1700, Honeywell Inc.) and a wheel encoder. Fig. 5(a) shows an example of segment of 3D point cloud data of a human after a distance-based clustering and segmentation. Figs. 5(b) and (c) show two 2D plane views, x-z axis view and x-y view. We use a center position (O) for object information for the further geometric description work. Fig. 6 depicts the geometric data
(b)
(c)
Fig. 5. Human segment of 3D point cloud data: (a) bird-eye view; (b) side view; (c) top view.
which is accumulated during 20 steps in this experiment. All values are estimated for mean and distribution using Gaussian mixture distribution method. Based on the figures, sphericity shows the lowest distribution among acquired geometric model values and Kurtosis with respect to centroid values are very big values. They are, respectively, 0.00012 and 1.3 ´ 1016 . They show totally different distribution. Fig. 7 shows the raw obtained object of all segments and vehicle track on the top view, and it shows raw track tracking results with two trajectories in front of an autonomous vehicle. The track tracking problems are clearly distinguishable when occlusion happens. Green points are a track of a vehicle. Blue points depict all of observed points including moving and static humans, obstacles. In the dotted rectangle, blue dots describe detected stationary objects such as poles. In experiments, there are four occlusion areas which have high risk for missing track. Fig. 8 shows experimental results of the enhanced object detection and tracking algorithm. It shows 2D plane and time step axis to verify two moving object tracks which is similar
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the number of points
sphericity
40 30 40
150 the number of points
sphericity 150 100
20 30
100 50
10 20 0 10 0 0 300
20 40 flatness 20 40 flatness
60
50 0 0
60
0 300
30 20
30 20
20 10
20 10
0.05 linearity 0.05 linearity
0.1 0.1
Fig. 8. The experimental results of the enhanced object detection and tracking.
10 10 0 0 0 0.5 1 0 0.5 1 std. dev. w.r.t. centroid 0 0 0.5 1 0 kur. w.r.t 0.5centroid 1 300 200 std. dev. w.r.t. centroid kur. w.r.t centroid 30 200 20 100 20 10 100 10 0 0 0 5 10 15 0 10 20 30 8 0 x 10 0 0 5 residual 10 planarity 15 dev. from normlaized 0 ave. 10 20 med 30 8 x 10 40 60 ave. dev. from med normlaized residual planarity 40 60 40
20
(b)
Fig. 9. The results of a conventional global nearest neighbor approach.
40 20
20
0 0 0 0
(a)
20
40
20
40
20 0 0 0 0
2 2
4 4
6 6
Fig. 6. The example of histogram of the accumulated geometric model data during 20 steps.
Fig. 10. The experimental results of the enhanced object detection and tracking.
Fig. 7. The initial acquired position data on the base frame. The data obtained using an LADAR, HDL-32e installed on an unmanaged ground vehicle. There are two moving human and random objects with four occlusion points.
as shown in Fig. 7. It explains only detected and estimated objects. The red and red color is representative identification of two objects . It intuitionally shows the proposed performance. Fig. 9 shows the moving object tracking results of a conventional global nearest neighbor approach. The figure shows fault two moving object tracks on 2D plane with track number axis as shown in Figs. 9(a) and (b). As shown in the figure, the track is missing when occlusion occurs. Each red track of Fig. 9 is two reference trajectories. If the estima-
K. Cho et al. / Journal of Mechanical Science and Technology 28 (11) (2014) 4381~4388
tion is correct, they should be one track. Fig. 10 shows the final results with vehicle track and estimated two moving humans. The red and black color tracks represent different objects. The green color track is the track of the vehicle.
4. Conclusions This paper describes the object detection and tracking method which enhances object occlusion situation using 3D information. The basic fundamental algorithms have several parameters which influence system performance. It shows the complementary solution between kinematic model and geometric model of multiple object tracking. It makes a tracking algorithm to enhance to occlude measurements and missing target tracking.
Nomenclature-----------------------------------------------------------------------LADAR GNN EKF r Nr Lm min{N r } min{Ps } min{as } xb , xv zb
Lm , L g
: Laser detection and ranging : Global nearest-neighbor : Extended Kalman filter : Radius for neighbor points : Number of neighbor points in radius r : Cluster of the m-th : Minimum number of neighbor points in the radius r. : Minimum number of points in an segment : Minimum number of layer in an segment : Vehicle‘s position on the base coordinate and the vehicle coordinate frames, respectively : Observation object’s position on the base coordinate frame : Statistical kinematic and geometric score : Track of vehicle and object of i-th
References [1] S. Blackman and R. Popoli, Design and analysis of modern tracking systems, Artech House, Norwood, MA, USA (1999). [2] S. Blackman, Multiple hypothesis tracking for multiple target tracking, IEEE Aerospace & Electronics Systems Magazine, 19 (1) (2004) 5-18. [3] K. O. Arras, O. M. Mozos and W. Burard, Using boosted features for the detection of people in 2D range data, Proc. of IEEE Robotics and Automation (2007) 3402-3407. [4] C. Premebida, O. Ludwid and U. Nunes, Exploiting LIDARbased features on pedestrian detection in urban scenarios, Proc. of 12th Int. IEEE Intelligent Transportation Systems (2009) 1-6. [5] L. E. Navarro-Serment, C. Mertz and M. Hebert, Pedestrian detection and tracking using three-dimensional LADAR data, The International Journal of Robotics Research, 29 (12) (2010) 1516-1528. [6] L. Spinello, M. Luber and K. O. Arras, Tracking people in 3D using a bottom-up top-down detector, Proc. of IEEE Int.
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Conf. on Robotics and Automation (2011). [7] K. Kidono, T. Miyasakam, A. Watanabe, T. Naito and J. Miura, Pedestrian recognition using high-definition LIDAR, Proc. of IEEE Intelligent Vehicles Symposium (2011) 405-410. [8] J. Xing, H. Ai and S. Lao, Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses, Proc. of IEEE Computer Vision and Pattern Recognition (2009) 1200-1207. [9] T. Yang, S. Z. Li, Q. Pan and J. Li, Real-time multiple objects tracking with occlusion handling in dynamic scenes, Proc. of IEEE Computer Vision and Pattern Recognition (2005) 970-975. [10] Z. Jiang, D. Q. Huynh, W. Moran and S. Challa, Appearance and motion based data association for pedestrian tracking, Image and Vision Computing New Zealand (2011) 459-464. [11] S. Luciano, O. A. Kai, T. Rudolph and S. Roland, A layered approach to people detection in 3D range data, Proc. of AAAI (2010). [12] K. Cho, C. Kim, S.-H. Baeg and S. Park, Leaned geometric features of 3D range data for human and tree recognition, Electronic Letters, 50 (3) (2014) 173-175. [13] A. Petrovskaya and S. Thrun, Model based vehicle detection and tracking for autonomous urban driving, Autonomous Robots, 26 (2-3) (2009) 123- 139. [14] B. Kalyan, K. W. Lee, S. Wijesoma, D. Moratuwage and N. M. Patrikalakis, A random finite set based detection and tracking using 3D LIDAR in dynamic environments, Proc. of IEEE Systems, Man and Cybernetics (2010) 2288-2292. [15] A. Azim and O. Aycard, Detection, classification and tracking of moving objects in a 3D environment, Proc. of IEEE Intelligent Vehicles Symposium (2012) 802-807. [16] L. Zhang, Q. Li, M. Li, Q. Mao and A. Nüchter, Multiple vehicle-like target tracking based on the Velodyne LiDAR, Proc. of IFAC Intelligent Autonomous Vehicles (2013) 126-131. [17] K. Cho, S.-H. Baeg and S. Park, Human and tree classification based on a model using 3D ladar in a GPS-denied environment, Proc. of SPIE 8741 Unmanned Syst. Technol. XV (2013). [18] Y. Bar-Shalom and W. D. Blair, Multitarget-Multisensor Tracking: Applications and Advances, vol. 3, Artech House, Norwood, MA (2000).
Kuk Cho received the Ph.D. from the department of intelligent robot engineering at University Science and Technology (UST) in 2014. He is currently working at KITECH, Korea. His research interests include 3D RGB-D data processing, environment recognition, machine learning, and autonomous mobile vehicles.
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Seung-Ho Baeg received the B.S. and M.S. degrees in Material Engineering at Korea University in 1991 and 1993, respectively. He is currently a principal researcher and a chief of robotic solution for extreme field agency on KITECH, Korea. His research interests include Laser Range Finder (LRF), 3D LADAR system, and robot system architecture design.
Sangdeok Park received his B.S. degree in Mechanical Design at Yeungnam University in 1998 and M.S. and Ph.D. degrees in Mechanical Engineering at Pohang University of Science and Technology (POSTECH) in 1990 and 2000, respectively. Since 2004, he has been with KITECH, Ansan, Korea, where he is currently a principal researcher with a chief officer of Robotics R&BD group of KITECH. His research interests include the design and control of quadruped walking robots, wearable robots and hydraulically driven robots.
