Multi-spectrum superpixel based obstacle detection under vegetation environments

Robust obstacle detection is an important task for unmanned ground vehide(UGV). Vegetation in off-road environments poses great challenges to this task. Usually, vegetation should not be considered as obstacles for off-road UGVs since they are soft and drivable. On the other hand, there are also possibilities that real obstacles exist in the vegetation, which makes the problem difficult. In this paper, a novel multi-spectrum data fusion based algorithm for partial occluded obstacle detection under complex vegetation environment is proposed. First a RGB and Near-infrared (NIR) multi-spectrum superpixel based segmentation strategy is employed to accurately segment the objects in the image. Obstacle candidate superpixels are then obtained through simple geometric computation in 3D laser data. Finally, the heterogeneous texture and 3D features are extracted from each candidate superpixel and fed in Support Vector Machine (SVM) to distinguish the real obstacles from vegetation. Experimental results on real data acquired from various vegetation environments demonstrate our success.

[1]  Lars Kuhnert,et al.  Spreading algorithm for efficient vegetation detection in cluttered outdoor environments , 2012, Robotics Auton. Syst..

[2]  Sungho Jo,et al.  Traversability Classification Using Super-voxel Method in Unstructured Terrain , 2014, RiTA.

[3]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Yunhui Liu,et al.  An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Lars Kuhnert,et al.  Structure overview of vegetation detection. A novel approach for efficient vegetation detection using an active lighting system , 2012, Robotics Auton. Syst..

[7]  Florentin Wörgötter,et al.  Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Lars Petersson,et al.  Classification of materials in natural scenes using multi-spectral images , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Jian Cheng,et al.  Robust vehicle detection using 3D Lidar under complex urban environment , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Michael Himmelsbach,et al.  Fast segmentation of 3D point clouds for ground vehicles , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[11]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[12]  Myung Jin Chung,et al.  Traversable ground detection based on geometric-featured voxel map , 2013, The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision.

[13]  Martial Hebert,et al.  Natural terrain classification using three‐dimensional ladar data for ground robot mobility , 2006, J. Field Robotics.

[14]  Achim J. Lilienthal,et al.  Path planning in 3D environments using the Normal Distributions Transform , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  G. Baudat,et al.  Kernel-based methods and function approximation , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[17]  David M. Bradley,et al.  Vegetation Detection for Driving in Complex Environments , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[18]  Martial Hebert,et al.  Natural terrain classification using 3-d ladar data , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[19]  George Vosselman,et al.  3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS , 2001 .

[20]  Kim L. Boyer,et al.  Linearized vegetation indices based on a formal statistical framework , 2004, IEEE Transactions on Geoscience and Remote Sensing.