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A scan restoration method for robust polar scanmatching in dynamic environmentsSeung-Hee Lee a , Heon-Cheol Lee & Beom-Hee Leea Department of Electrical and Computer Engineering , Seoul National University , 1Gwanak-ro, Gwanak-gu , Seoul , 151-742 , KoreaPublished online: 01 May 2013.
To cite this article: Seung-Hee Lee , Heon-Cheol Lee & Beom-Hee Lee (2013) A scan restoration method for robust polar scanmatching in dynamic environments, Advanced Robotics, 27:11, 877-891, DOI: 10.1080/01691864.2013.791741
To link to this article: http://dx.doi.org/10.1080/01691864.2013.791741
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A scan restoration method for robust polar scan matching in dynamic environments
Seung-Hee Lee, Heon-Cheol Lee* and Beom-Hee Lee
Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea
(Received 13 June 2012; accepted 18 August 2012)
This paper addresses the problem of scan matching which is highly indispensable for mobile robot systems based onrange sensors. Recently, polar scan matching (PSM) has been used in solving the problem because it is accurate and fastenough to be performed in real time. However, the performance of PSM degenerates when the portion of scan data fromdynamic objects is excessively large. This paper proposes a scan restoration method to overcome this problem andimprove the performance of PSM in dynamic environments. The proposed method restores the scan data from dynamicobjects to appropriate scan data from static objects. First, whole scan data is segmented and classified as static anddynamic objects. Next, curvature functions are extracted from the classified segments and smoothed by interpolating thesegments indicating dynamic obstacles. PSM with the proposed method was tested and evaluated in various real dynamicenvironments, which reveals that the proposed method can improve the performance of PSM in dynamic environments.
Keywords: polar scan matching; curvature function; interpolation; scan restoration method
Laser scan matching is a key technique for the localiza-tion of mobile robots by matching two laser scan dataobtained from a mobile robot with a laser range finder(LRF). The basic concept of laser scan matchinginvolves estimating the differences in mobile robot posesbetween two times when two laser scan data wereobtained. There are three categories of laser scan match-ing techniques according to the concept of data associa-tion. First, point-to-point matching is a methodology thatfinds corresponding points between two laser scan dataand matches scan data with these points. One such tech-nique is called iterative closest point (ICP), whichiteratively finds corresponding points of laser scan databy searching the minimum Euclidean distance betweenthe scan points of another laser scan data. Similarly, Luet al.  suggested another way called iterative matchingrange point (IMRP) that finds the corresponding pointsby introducing a matching range based on the LaserRange Finder (LRF) coordinate system at a certain time.They also proposed iterative dual correspondence (IDC)which simultaneously uses both ICP and IMRP. Otherscan matching techniques are based on point-to-featurematching. Here the features indicate lines, curves, orrange extrema that are extracted from scan data. Cox proposed one of the point-to-feature scan matching tech-niques that extracts features by calculating the Gaussianmean and variance of each scan point that belongs to the
grid map segments. Cox then matched the scan pointswith the extracted features. Finally, a feature-to-featurematching technique matches the features extracted fromtwo scan data. Weiss et al.  proposed the histo-grams of scan data as the features. They suggested away to match two scan data by calculating the phase thatmaximizes a cross-correlation between two histograms.
Generally, the conventional point-to-feature or fea-ture-to-feature laser scan matching techniques are notappropriate to be used in real-time localization of amobile robot because they have to compare all of thepre-learned features with the scan data in every matchingprocess, causing a heavy computational load. Point-to-point scan matching techniques should be used in thiscase. Diosi et al. [6,7] proposed another point-to-pointscan matching technique, polar scan matching (PSM),which matches the range values of LRF bearingsbetween two scan data in the polar coordinate systeminstead of finding corresponding scan points in the Carte-sian coordinate system. PSM projects one scan data toanother scan data and resamples the projected scan datato rearrange the scan points in the same LRF bearings.The computational load of PSM is much less than thatof ICP or IDC. Also, as previously shown,[6,7] PSMcan reject occlusions by segmenting the scan data.
Although PSM can perform scan matching despiteocclusion, it fails to perform matching when there aremore than two dynamic obstacles, a problem that causes
*Corresponding author. Email: firstname.lastname@example.org
Advanced Robotics, 2013Vol. 27, No. 11, 877891, http://dx.doi.org/10.1080/01691864.2013.791741
2013 Taylor & Francis and The Robotics Society of Japan
the limitation of robots not being able to appropriatelyperform tasks in dynamic environments. In the future,such robots will coexist with people and other robots, sothe robotics technologies that are working robustly indynamic environments should be developed. This paperproposes an approach that can overcome this limitation,called the scan restoration method, which makes scanpoints that indicate dynamic obstacles or occlusions tobe restored to nearby static obstacles like walls or corri-dors. This method segments the scan data not just bycalculating the Euclidean distance between two scanpoints suggested in PSM but by considering the nearbyenvironmental structure and noise of the LRF using anadaptive breakpoint detector (ABD). After segmenta-tion, restoration targets are chosen by some standards forselection, and curvature functions of scan data are thenextracted based on the Cartesian coordinate system.Curvature functions can be considered as scan data fea-tures, and Nunez et al.  suggested a feature-to-featurescan matching algorithm using curvature functions asfeatures. In the proposed method, the curvature values ofthe restoration target are interpolated using nearby curva-ture values of two non-restoration targets. All segmentsof the scan data indicating dynamic obstacles arerestored to static environments using the above pro-cesses, and PSM can perform scan matching robustly inthe dynamic environment using these restored scan data.The main idea of the scan restoration method was origi-nated by Gil et al. , who suggested a way to restorethe original shape of a noisy object using curvatureflows. Similarly, scan restoration method considers thelaser scan data as objects that must be restored, andregards dynamic obstacles or occlusions as the noisesources that scatter the original data. Experiments invarious real dynamic environments have shown that theproposed method restored the scan points, indicating sta-tic environments that were hidden by dynamic obstacles,and PSM using these restored scan data estimated thepose difference correctly. Also, the proposed method wasverified as being much faster than the PSM scan match-ing processes.
This paper is organized as follows. The erroneousscan matching result of PSM in the dynamic environmentis described with an example in Section 2. In Section 3,the way to implement the proposed method is summa-rized. The experimental results are shown in Section 4.And finally, the conclusions are presented in Section 5.
2. Problem description
This section describes the problem of PSM, which failsto perform scan matching in dynamic environments. Anexample of a situation that a mobile robot moves in adynamic environment and the corresponding two laserscan data are shown in Figure 1(a) and (b), respectively.
When dynamic obstacles exist in the same environment,corridors are hidden by them and do not stand in thesame position in the two scan data. Due to theocclusions, PSM fails to match the current scan C intothe reference scan R as shown in Figure 1(c). Thisfailure is caused in the process of the pose estimation ofPSM, and the analysis of this problem is describedbelow. Notations used in PSM are summarized inTable 1.
First, C is projected into the frame of R. The polarcoordinates of the i-th scan point in the projected currentscan C0 are computed as follows:
r0C;i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi(rC;i cos (hC /C;i) xC)2 (rC;i sin (hC /C;i) yC)2
/0C;i a tan2(rC;i sin (hC /C;i) yC; rC;icos (hC /C;i) xC) 2
Then, the projected current scan C0 is resampled bythe linear interpolation in the polar coordinate system toalign /R;i and /
0C;i. For the resampled current scan
C00 f(r00C;i;/00C;i)ji 1; ;Ng where N is the numberof current scan points, the translation estimation can beperformed by
375 H DxCDyC
where v is a noise vector, (DxC;DyC) is the estimatedpose difference between the times corresponding R andC. The Jacobian matrix H is computed as follows:
cos/r;1 sin/r;1... ..
If moving obstacles stand at different locations intwo times, the error of range values indicating the samespots in real environments becomes serious regardless ofthe robot movement. For this reason, projection via (1),(2), or estimation via (4) cannot derive an accurateresult. Similarly, the orientation estimation is done byshifting C00 to the left or right side in the polar coordi-nate system to find the shift angle value that creates aminimum distribution error between R and C00. In thesame manner explained above, if R and C00 arecontaminated by the moving obstacles, a correct shiftvalue cannot be derived because the local minimum isoccurred despite of many iteration steps.
878 S.-H. Lee et al.
There are several approaches to solve these problems,most of which suggest completely new scan matchingalgorithms or get help from other sensors. Zhao et al. proposed the Global Positioning System (GPS) asan assistant sensor to improve localization accuracy.They also suggested the classification method of the scandata segment, in which the segments are sorted intomoving, static, new, or seed states, and updated themusing a Bayesian filter each step. Rizzini et al. adopted the Adaboost algorithm to locate human data inthe scan data using 14 predefined features. All segmentsare iteratively compared to predefined features andhuman-like segments are rejected from scan matchingprocess. Lingemann et al.  and Kim et al. suggested new scan matching algorithms, HAYAIand CVFSAC, respectively, which can perform scanmatching in the dynamic environment.
In this paper, we suggest a scan restoration methodwhich can be easily applied to conventional scan match-ing algorithms by handling only raw laser scan data. Theproposed method restores the scan points indicatingdynamic obstacles as static environments according tothe following criteria. We defined a restoration targetindicating a dynamic object as the segment which has arelatively small portion of scan data compared to nearbysegments indicating static environments. Therefore, somestatic obstacles occupying small portions against theentire scan data can be also regarded as the restorationtargets. This is reasonable because both the segmentswhich indicate static and dynamic obstacle occupying
small portions may degenerate the performance of scanmatching algorithm. Although the proposed method isdescribed with PSM in this paper, we expect that theproposed method can be applied to not only PSM butalso other scan matching algorithms to improve therobustness of scan matching in dynamic environments.
3. Scan restoration method
A scan restoration method is a pre-processing scan datastep that should be performed before all scan matchingprocesses. The purpose of this method is to obtain therestored scan data which is refined data from the originalscan data in order to improve the performance of scanmatching. Firstly, the proposed method performs asegmentation process on both scans R and C to find therestoration targets indicating the dynamic obstacles.Then, the curvature functions for both scans areextracted. The curvature values corresponding to the scandata points of the restoration targets are interpolatedalong the curvature values of the non-restoration targets.This section des...