Localization of an unmanned ground vehicle using 3D registration of laser range data and DSM

A computer vision technique to identify the location of an outdoor unmanned ground vehicle (UGV) is presented. The proposed technique is based on hybrid 3D registration of 360 degree laser range data to a digital surface model (DSM). Range frames obtained from 48 laser detectors are aligned with the reference coordinate system of the DSM. Three novel approaches are proposed for accurate and fast 3D registration of range data and the DSM. First, a two-step hybrid 3D registration technique is proposed. A pair-wise registration step of two consecutive range frames is followed by a refinement step using a layered DSM. Second, a fast projection-based pair-wise registration is proposed by employing rasterized 360 degree range frames. Third, a high elevation DSM is divided into several elevation layers and correspondence search is done near the vehicle's current elevation. This reduces the number of matching outliers and facilitates fast localization. Experimental results show that the proposed approaches yield better performance in 3D localization compared to conventional 3D registration techniques. Error analysis on five outdoor paths is presented with respect to ground truth.

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“…Then, we need to find a matching point Q' in the destination. In this paper, a modified IPP method is used for a fast search [15].…”

Section: Projection-based Pair-wise Registrationmentioning

confidence: 99%

“…The initial map is then refined to assist the autonomous driving of the vehicle in a multi-level parking garage. In one of our previous investigations, 360 degree range data is registered to a DSM in a global coordinate system [15].…”

Section: Introductionmentioning

confidence: 99%

Localization of an unmanned ground vehicle based on hybrid 3D registration of 360 degree range data and DSM

Park

Choi

Moon

et al. 2011

Int. J. Control Autom. Syst.

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“…However, accumulation errors due to the registration of a long sequence of range data could yield erroneous localization. To overcome this problem, a global registration is done next (Park and Choi, 2009).…”

Section: Localization Schemementioning

confidence: 99%

“…An ICP-based method is used in the second step. However, the DSM is divided into multiple elevation layers to speed up the global registration (Park and Choi, 2009).…”

Section: Projection-based Icpmentioning

confidence: 99%

“…(Kummerle et al, 2009) present an autonomous driving technique of an unmanned vehicle which is equipped with multiple navigation sensors including a 360 degree range sensor. In our previous work, a 3D registration technique is introduced to align 360 degree range data and a digital surface model (DSM) (Park and Choi, 2009). Matching 3D maps obtained from different coordinate systems requires a reasonable number of correspondences between the maps (Hartley and Zisserman, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Localization of an Unmanned Ground Vehicle Using a 360 Degree Range Sensor

Park

Choi

2011

Proceedings of the International Conference on Computer Vision Theory and Applications

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Federated‐filter‐based unmanned ground vehicle localization using 3D range registration with digital elevation model in outdoor environments

Choi

Park

Kim

et al. 2012

Journal of Field Robotics

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An integrated GPS/INS does not guarantee localization robustness in outdoor environments, because GPS is vulnerable to external disturbances. However, a digital elevation model (DEM) contains 3D data on the terrain over a specified area and hence can provide in‐depth localization information during GPS blockage. This paper proposes federated‐filter‐based localization using three‐dimensional (3D) range registration with a DEM. A no‐reset‐feedback method is used and a 3D LIDAR sensor, magnetic compass, and odometer are used to correct INS errors in GPS blockage. For 3D range registration with DEM, this paper presents a framework based on the weighted registration scheme of two transformations, pairwise registration and registration with DEM, with the INS position and attitude information. The transformation is first determined by comparing the results of two registration methods with the INS position and is then modified to replace the orientation result of 3D registration with the INS attitude. A multilayered DEM approach using the height of the integrated system is also used to constrain the search range of DEM into three layers near the current unmanned ground vehicle (UGV) position when the corresponding point is searched for in the DEM. Experimental results show that the proposed localization algorithm can greatly enhance the robustness and accuracy of UGV localization in outdoor environments. © 2012 Wiley Periodicals, Inc.

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Localization of an unmanned ground vehicle using 3D registration of laser range data and DSM

Cited by 7 publications

References 13 publications

Localization of an unmanned ground vehicle based on hybrid 3D registration of 360 degree range data and DSM

Localization of an unmanned ground vehicle based on hybrid 3D registration of 360 degree range data and DSM

Real-Time Localization of an Unmanned Ground Vehicle Using a 360 Degree Range Sensor

Federated‐filter‐based unmanned ground vehicle localization using 3D range registration with digital elevation model in outdoor environments

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