Dynamic Obstacle Detection Using Visually Informed Scan Matching

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  • Dynamic Obstacle Detection using Visually Informed Scan Matching

    Tae-Seok Leea, Heon-Cheol Lee, Won-Sok Yoo, Doojin Kim and Beom-Hee Lee

    ASRI, Seoul National University, Korea

    afelix84@snu.ac.kr

    Keywords: Dynamic obstacle detection, Polar scan matching (PSM), Mobile robot

    Abstract. This paper presents a real-time dynamic obstacle detection algorithm using a scan matching

    method considering image information from a mobile robot equipped with a camera and a laser

    scanner. By combining image and laser scan data, we extract a scan segment corresponding to the

    dynamic obstacle. To complement the performance of scan matching, poor in dynamic environments,

    the extracted scan segment is temporarily removed. After obtaining a good robot position, the position

    of the dynamic obstacle is calculated based on the robots position. Through two experimental

    scenarios, the performance of the proposed algorithm is demonstrated.

    Introduction

    As mobile robots are employed in many real applications, the robotics researches for operating robots

    in real complex environments have been studied. To conduct missions in complex environments, the

    robot should understand the environments which changes over time. Especially when the robots

    interact with humans in same space, the robot should be able to perceive moving objects. Therefore,

    recognition and identification of moving object is important technique. A moving obstacle detection

    technique in dynamic environment of a single mobile robot is developed in this paper.

    Moving object detection using image information is a classic problem in the field of computer

    vision techniques. There are numerous algorithms as pedestrians and vehicles tracking, object

    recognition, however, usually the motion pattern or basic image model of the moving object is

    necessary [1-3]. On the other hand, the algorithm does not require a priori information such as optical

    flow is inaccurate for a mobile robot in real application. Regardless of whether detection algorithm is

    used, we need exact camera calibration depending on the hardware settings or disparity information

    from stereo camera to acquire position of moving object from image data.

    The accuracy of environmental information obtained from a mobile robot is based on the exact

    robot location, so it also highly associated with Simultaneous Localization and Mapping (SLAM)

    technique. Recently, the visual SLAM is actively studied, however it is hard to implement in real-time

    because of its large computational load. In this reason, scan matching method using a laser scanner is

    popular because a laser scanner has fast in data acquisition, fine resolution and high accuracy [4-6].

    Many of previous researches have assumptions about dynamic model, and tracking the dynamic

    obstacle using corrected robot position after scan matching. However, occlusions and moving scan

    data may cause performance degradation, so filtering dynamic scan data, comes from a moving

    obstacle, before scan matching is required to improve the performance of scan matching.

    In this paper, a visually informed scan matching method fusing image information and laser scan

    data is performed to estimate the position of the dynamic obstacle. We predict the location of dynamic

    obstacle by image input and use the prediction value to adjust the laser scan data. Then, scan matching

    is conducted. The position of the dynamic obstacle is updated based on the robot position from the

    scan matching result. It is assumed that we have no priori information about the environments and the

    dynamic obstacle. To apply in real environments, we adopt Polar Scan Matching (PSM) for scan

    matching, because of its fast calculation time [7].

    The following section describes how to extract the estimated dynamic obstacle direction using

    image data. In the third section, we introduce the visually informed scan matching method with the

    estimated dynamic obstacle direction. The proposed algorithm is validated by experiments, and

    conclusions are addressed in the last part of this paper.

    Applied Mechanics and Materials Vols. 313-314 (2013) pp 1192-1196Online available since 2013/Mar/25 at www.scientific.net (2013) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMM.313-314.1192

    All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP,www.ttp.net. (ID: 130.207.50.37, Georgia Tech Library, Atlanta, USA-16/11/14,05:23:12)

    http://www.scientific.nethttp://www.ttp.net

  • Estimated Dynamic Obstacle Direction

    Optical flow is one of the general methods that can find the movement of the obstacle without any

    prior knowledge about the obstacle. It is the algorithm that numerically expresses the relative motion

    between the camera and the scene, and used for motion detection, object segmentation and other

    applications. In this research, optical flow is applied in continuous input images, and then the pixels

    with the image flow vector over the threshold value are left. is proportional to the speed of the

    robot |vr| and the speed of the dynamic obstacle |vr|, and inverse to the distance between the robot and

    the obstacle dro as follows:

    (initial state)r o

    r

    ro

    v vv

    d

    (1)

    However, there is no dynamic obstacle information at the initial states, therefore, we can set the

    threshold value based on the speed of the robot.

    Fig. 1. Detected pixels by optical flow and their

    image vector flow(red dots and lines).

    Fig. 2. Relation between object and robot.

    When the image flow vectors are generated as shown in Fig. 1, the pixel coordinates which has

    larger vector magnitude than are extracted. We can calculate the relative angle between the robot

    and dynamic obstacle by assuming the center position of the extracted pixel coordinates is the center

    point of the obstacle. As shown in Fig. 2, by assuming that the surface of the obstacle is a plane and

    the normal vector of the plane is toward the robot, the value in (2) is the relative angle between the

    robot and the dynamic obstacle.

    1 2cos sin 0 cos sin

    ( ) ( )sin cos 0 1 sin cos

    t

    tA HR T R

    = =

    (2)

    A is the homography matrix of the plane, is the zoom parameter, is the longitude, is the rotation

    parameter, is the latitude and =arccos(1/t). The details about this issue are beyond the scope of this

    paper.

    Because we have no knowledge about the obstacle model, we cannot assure the calculated relative

    angle is exact value. Therefore, the acquired relative angle is named the estimated dynamic obstacle

    direction.

    Visually Informed Scan Matching

    Linear scan restoration using estimated dynamic obstacle direction. Diosi and Kleeman [7]

    developed PSM which is a kind of point-to-point scan matching techniques. PSM not only takes the

    advantage of the structure of the laser measurements but also eliminates an expensive search for

    corresponding points differently from the standard ICP. The outstanding point of PSM is its low

    computation time even though it requires an iteration process. Scan pre-processing to eliminate

    outliers of a reference scan and a current scan is the basic step of PSM. After that, the current scan is

    projected into the reference scan coordinates. The next step is to estimate translations in the x and y

    directions. Here, PSM is generally aided by a robot odometer for the sake of obtaining a possible

    angle p. For the projected points P = {pi} by the possible angle, translations are estimated as follows:

    [ ] 2[ ]

    arg min ( , )c c

    X Y i i i c cx y i

    T T w r p x y

    = (3)

    Applied Mechanics and Materials Vols. 313-314 1193

  • where pi(xc,yc) is the point translated by xc and yc from the i-th projected current scan point. Finally,

    the rotation angle is improved by a quadratic interpolation method. When the change amount from the

    initially obtained possible angle is , the final rotation angle is computed by

    O p b = + (4)

    where b is a heuristic constant. The detailed description of PSM is stated in the literature [7].

    (a) (b)

    Fig. 3. (a) Mismatched scan, (b) Large error in

    rotation angle.

    (a) (b)

    Fig. 4. (a) Laser scan data with dynamic obstacle

    (thick line), (b) Restored laser scan data (thick line) at

    same time.

    However, the proportion of the dynamic obstacle in the scan data becomes large, the point-to-point

    matching portion of PSM is reduced. In this case, as shown in Fig. 3, it is difficult to obtain accurate

    matching results. The other hand, more correct scan matching results can be obtained by removing

    and restoring the laser scan data of the dynamic obstacle.

    First, segmentation is conducted according to the continuity of laser scan data. After that, the laser

    scan segment which has the same direction with the estimated dynamic obstacle direction from the

    above section is selected for restore. The selected laser scan segment is changed by taking linear

    interpolation based on the value of each side of the segment. In this manner, the raw laser scan data as

    in Fig. 4(a) changes to the restored laser scan as in Fig. 4(b).

    Visually informed PSM and dynamic obstacle detection. In this research, the general PSM

    using raw scan data is not used, the visually informed PSM which adjusts laser scan data

    corresponding to image information about the dynamic obstacle is conducted. The environmental

    information is updated based on the robot position acquired from the proposed PSM method. The

    original data of the linearly interpolated scan segment can be represented in global coordinates, and it

    means the position of the dynamic obstacle. Through comparison with the previous frame, the

    velocity of the dynamic obstacle could be measured. The dynamic obstacle detection using visually

    informed PSM follows the flowchart in Fig. 5. The performance of the visually informed PSM is

    presented in next section.

    Fig. 5. Flowchart of the visually informed PSM and dynamic obstacle detection.

    Experimental Results

    The experiments are performed in two scenarios in Fig. 6. As shown in Fig. 7, the space is

    490cm800cm and surrounded by walls on three sides. The mobile robot, Pioneer 3DX, equipped

    with Sony SNC-RZ50 camera and Hokuyo UTM-30LS laser scanner was driven straight forward with

    20cm/s. The dynamic obstacle was driven 1) 45 and 2) perpendicular to the robots path. The

    1194 Machinery Electronics and Control Engineering II

  • obstacle has 80cm diameter and kept 15cm/s. The obstacle moved 5 seconds after the robot started.

    Visually informed PSM was conducted every 0.1 seconds. The proposed method, visually informed

    PSM, is compared with the general PSM without restoration for each scenario.

    (a) (b)

    Fig. 6. Experimental environments: (a) Obstacle

    accesses to 45 to robots path, (b) Obstacle passes

    robots path vertically.

    Fig. 7. Snapshots of scenario 1.

    (a)

    (b)

    (c)

    (d)

    t=6s t=8s t=10s t=12s

    Fig. 8. Environmental map along time (thick line: restored laser scan): (a) Visually informed PSM with

    scenario 1, (b) General PSM with scenario 1, (c) Visually informed PSM with scenario 2, (d) General PSM

    with scenario 2.

    (a) (b)

    Fig. 9. Transition of dynamic obstacle and estimated values: (a) Scenario 1, (b) Scenario 2.

    As shown in Fig. 8, visually informed PSM presents more accurate result in both scenarios,

    because the scan segment of the dynamic obstacle was removed. As the obstacle closes to the robot,

    large portion of the scan data becomes the dynamic segment, and then the rotation error of the general

    PSM significantly increases. The transition of the dynamic obstacle obtained from two PSM methods

    are represented in Fig. 9. The position error at the end point is small in the general PSM case.

    However, the dynamic obstacle cannot be tracked continuously with the general PSM, because the

    Applied Mechanics and Materials Vols. 313-314 1195

  • deviation is very large as stated in Table 1. On the other hand, we can estimate the dynamic obstacle

    using visually informed PSM due to its small deviation. If the rotation error of the general PSM is

    corrected, then the estimated coordinates of the obstacle may have greater error than results of the

    visually informed PSM. The performance of proposed algorithm is powerful, since the average error

    of the obstacles position is about 10% and the standard deviation is about 2% of the results of the

    conventional PSM. Because the scenario 1 gives larger size of the dynamic segment than the scenario

    2, the errors tend to increase.

    Table 1Error of dynamic obstacle's position. (Unit: cm)

    Visually informed PSM General PSM

    Scenario 1

    (45 path)

    Average 61.27 496.49

    Standard deviation 42.67 1698.67

    Scenario 2

    (Perpendicular)

    Average 36.73 343.52

    Standard deviation 21.56 936.36

    Conclusion

    In this paper, we develop visually informed PSM which is effective for detecting a moving obstacle in

    dynamic environments. To find the obstacle with no priori information, image information and scan

    data are considered together. Estimated dynamic obstacles direction by optical flow method is used

    to segment the laser scan, and then the segmented scan data is applied to PSM after restoration. As a

    result of experiments, the proposed algorithm presents about 0.12 times average error than error of the

    general PSM.

    Acknowledgement

    This work was supported in part by a Korea Science and Engineering Foundation (KOSEF) NRL

    Program grant funded by a Korean government (MEST) (No.R0A-2008-000-20004-0), in part by the

    Brain Korea 21 Project, and in part by the Industrial Foundation Technology Development Program

    of MKE/KEIT [Development of CIRT(Collective Intelligence Robot Technologies)].

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    [2] A. Ess, B. Leibe, K. Schindler, and L. Van Gool: A mobile vision system for robust multi-person

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    [3] D. M. Gavrila, and S. Munder: Multi-cue pedestrian detection and tracking from a moving

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    [4] C. Wang, C. Thorpe, S. Thrun, M. Hebert, and H. Durrant-whyte: Simultaneous localization,

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    [5] M. Becker, R. Hall, S. Kolski, K. Macek, R. Siegwart, and B. Jensen: 2D laser-based

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