Sensor fusion for intelligent behavior on small unmanned ground vehicles

Sensors commonly mounted on small unmanned ground vehicles (UGVs) include visible light and thermal cameras, scanning LIDAR, and ranging sonar. Sensor data from these sensors is vital to emerging autonomous robotic behaviors. However, sensor data from any given sensor can become noisy or erroneous under a range of conditions, reducing the reliability of autonomous operations. We seek to increase this reliability through data fusion. Data fusion includes characterizing the strengths and weaknesses of each sensor modality and combining their data in a way such that the result of the data fusion provides more accurate data than any single sensor. We describe data fusion efforts applied to two autonomous behaviors: leader-follower and human presence detection. The behaviors are implemented and tested in a variety of realistic conditions.

[1]  G. Kogut,et al.  Using advanced computer vision algorithms on small mobile robots , 2006, SPIE Defense + Commercial Sensing.

[2]  Stergios I. Roumeliotis,et al.  Sensors and algorithms for small robot leader/follower behavior , 2001, SPIE Defense + Commercial Sensing.

[3]  Greg Kogut,et al.  A Segway RMP-based robotic transport system , 2004, SPIE Optics East.

[4]  I. Fujimasa,et al.  Development of a new infrared imaging system: an infrared image superimposed on the visible image , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[5]  P. Verlinde,et al.  Multi-level Data Fusion for the Detection of Targets Using Multi-spectral Image Sequences , 1998 .

[6]  H. D. Arlowe.  Thermal detection contrast of human targets , 1992, Proceedings 1992 International Carnahan Conference on Security Technology: Crime Countermeasures.

[7]  Ronald L. Boring,et al.  "Turn Off the Television!": Real-World Robotic Exploration Experiments with a Virtual 3-D Display , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[8]  James M. Rehg,et al.  Statistical Color Models with Application to Skin Detection , 2004, International Journal of Computer Vision.

[9]  Kurt Konolige,et al.  Markov Localization using Correlation , 1999, IJCAI.

[10]  B. Sights,et al.  Integrated Control Strategies Supporting Autonomous Functionalities in Mobile Robots , 2005 .

[11]  Donald D. Dudenhoeffer,et al.  Mixed-initiative control for remote characterization of hazardous environments , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[12]  G. Gilbreath,et al.  Accurate Waypoint Navigation using Non-Differential GPS , 2002 .

[13]  Charles W. Therrien,et al.  An adaptive technique for the enhanced fusion of low-light visible with uncooled thermal infrared imagery , 1997, Proceedings of International Conference on Image Processing.

[14]  Hobart R. Everett,et al.  Transitioning unmanned ground vehicle research technologies , 2005, SPIE Defense + Commercial Sensing.

[15]  Eli Saber,et al.  Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions , 1998, Pattern Recognit. Lett..

[16]  Hobart R. Everett,et al.  Modular robotic intelligence system based on fuzzy reasoning and state machine sequencing , 2007, SPIE Defense + Commercial Sensing.