Modeling and simulations of three-dimensional laser imaging based on space-variant structure

Abstract A three-dimensional (3D) laser imaging system based on time of flight is proposed, based on the human retina structure. The system obtains 3D images with space-variant resolution, and we further establish mathematical models of the system and carried out simulative comparison between space-variant structure (SVS) and space-invariant structure (SIS). The system based on SVS produces significant improvements over traditional system based on SIS in the following aspects: (1) The system based on SVS uses less pixels than that based on SIS under the same field of view (FOV) and resolution. Therefore, this property is more suitable for uses in situations that require high speed and large volume data processing. (2) The system based on SVS has higher efficiency of utilization of photodiode array than that based on SIS. (3) 3D image based on SVS has the properties of rotation and scaling invariance. (4) The system based on SVS has higher echo power in outside ring of large photodiode array, which is more effective in detecting targets with low reflectance.

[1]  Tomas Chevalier,et al.  Laser radar modeling for simulation and performance evaluation , 2009, Security + Defence.

[2]  Michael Jack,et al.  MBE based HgCdTe APDs and 3D LADAR sensors , 2007, SPIE Defense + Commercial Sensing.

[3]  Stephen C. Cain,et al.  Direct-Detection Ladar Systems , 2010 .

[4]  Ping Yuan,et al.  Large format geiger-mode avalanche photodiode LADAR camera , 2013, Defense, Security, and Sensing.

[5]  Qun Hao,et al.  Differential optical-path approach to improve signal-to-noise ratio of pulsed-laser range finding. , 2014, Optics express.

[6]  G. Buller,et al.  Kilometer-range, high resolution depth imaging via 1560 nm wavelength single-photon detection. , 2013, Optics express.

[7]  Zheng Niu,et al.  Range determination for generating point clouds from airborne small footprint LiDAR waveforms. , 2012, Optics express.

[8]  Paul F. McManamon,et al.  Review of ladar: a historic, yet emerging, sensor technology with rich phenomenology , 2012 .

[9]  Simon Foster,et al.  Optics , 1981, Arch. Formal Proofs.

[10]  E. L. Schwartz,et al.  Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception , 1977, Biological Cybernetics.

[11]  Kaichang Di,et al.  A continuative variable resolution digital elevation model for ground-based photogrammetry , 2014, Comput. Geosci..

[12]  P. Roelfsema,et al.  Automatic spread of attentional response modulation along Gestalt criteria in primary visual cortex , 2011, Nature Neuroscience.

[13]  William T. Estler,et al.  Measurement technologies for precision positioning , 2015 .

[14]  Filiberto Pla,et al.  Log-polar mapping template design: From task-level requirements to geometry parameters , 2008, Image Vis. Comput..

[15]  Brent Schwarz Mapping the world in 3D: LIDAR , 2010 .

[16]  M. Meister,et al.  Dynamic predictive coding by the retina , 2005, Nature.

[17]  Luis Nino-de-Rivera,et al.  Visual simulation of retinal images through microstructures , 2012 .

[18]  Alexandre Bernardino,et al.  A review of log-polar imaging for visual perception in robotics , 2010, Robotics and Autonomous Systems.

[19]  Brent Schwarz,et al.  LIDAR: Mapping the world in 3D , 2010 .

[20]  Steven Eric Johnson Effect of target surface orientation on the range precision of laser detection and ranging systems , 2009 .

[21]  H. Basford,et al.  Optimal eye movement strategies in visual search , 2005 .