A novel outdoor scene-understanding framework for unmanned ground vehicles with 3D laser scanners

Abstract

Outdoor scene understanding plays a key role for unmanned ground vehicles (UGVs) to navigate in complex urban environments. This paper presents a novel 3D scene-understanding framework for UGVs to handle uncertain and changing lighting conditions outdoors. A 2D bearing angle (BA) image is deployed to perform scene understanding so that the computational burden in the process of segmentation and classification of the 3D laser point cloud can be reduced. An improved super-pixel algorithm is used for fast 3D scene segmentation, and then the Gentle–Adaboost algorithm is utilized to perform super-pixel patch classification using the texture features of the Gray Level Co-occurrence Matrix. All false classification results in the uncertain super-pixel patches of BA images are transformed back to raw 3D laser points and a re-classification is conducted to refine the 3D scene understanding for UGVs. The results from a real laser dataset taken from a large-scale campus environment show the validity and robust performance of the proposed approach, in comparison with the results from Korea Advanced Institute of Science and Technology dataset.

Keywords

3D point clouds laser scanner unmanned ground vehicles urban scene understanding

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References

Achanta

Shaji

Smith

. (2012) SLIC super-pixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 34: 2274–2282.

Aijazi

Checchin

Trassoudaine

(2013) Segmentation based classification of 3D urban point clouds: a super-voxel based approach with evaluation. Remote Sensing 5: 1624–1650.

Behley

Steinhage

Cremers

(2012) Performance of histogram descriptors for the classification of 3D laser range data in urban environments. In: Proceeding of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4391–4398.

Bharati

Liu

MacGregor

(2004) Image texture analysis: methods and comparisons. Chemometrics and Intelligent Laboratory Systems 72: 57–71.

Choe

Shim

Chung

(2013) Urban structure classification using the 3D normal distribution transform for practical robot applications. Journal of Advanced Robotics 27: 351–371.

Floros

Leibe

(2012) Joint 2D-3D temporally consistent semantic segmentation of street scenes. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2823–22830.

Haralick

Shanmugam

Dinstein

(1973) Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3: 610–621.

Himmelsbach

Luettel

Wuensche

(2009) Real-time object classification in 3D point clouds using point feature histograms. In: Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 994–1000.

Johnson

(1967) Hierarchical clustering schemes. Psycsetrika 32: 241–254.

10.

Socher

Fei-Fei

(2009) Towards total scene understanding: classification, annotation and segmentation in an automatic framework. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2036–2043.

11.

Lienhart

Kuranov

Pisarevsky

(2003) Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: DAGM’03, 25th Pattern Recognition Symposium, Madgeburg, Germany, September, pp. 297–304. Berlin: Springer, pp. 297–304.

12.

Munoz

Bagnell

Vandapel

. (2009a) Contextual classification with functional Max-Margin Markov Networks. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 975–982.

13.

Munoz

Vandapel

Hebert

(2009b) Onboard contextual classification of 3D point clouds with learned high-order Markov Random Fields. In: Proceeding of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2009–2016.

14.

Murtagh

Contreras

(2012) Algorithms for hierarchical clustering: an overview. Data Mining Knowledge Discovery 2: 86–97.

15.

Ponsa

Serrat

López

(2011) On-board image-based vehicle detection and tracking. Transactions of the Institute of Measurement and Control 33: 783–805.

16.

Scaramuzza

Harati

Siegwart

(2007) Extrinsic self calibration of a camera and a 3D laser range finder from natural scenes. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), San Diego, CA, pp. 4164–4169.

17.

Schapire

(2003) The boosting approach to machine learning: an overview. In: Nonlinear Estimation and Classification. Berlin: Springer, pp. 149–171.

18.

Tan

Kong

Liang

(2013) A 3D object model for wireless camera networks with network constraints. Transactions of the Institute of Measurement and Control 35: 866–874.

19.

Zhuang

Jiang

. (2013) 3D-laser-based scene measurement and place recognition for mobile robot in dynamic indoor environment. IEEE Transactions on Instrumentation and Measurement 62: 438–450.