Time-Gated Topographic LIDAR Scene Simulation

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been developed at the RochesterInstitute of Technology (RIT) for over a decade. The model is an established, first-principles based scene simulationtool that has been focused on passive multi- and hyper-spectral sensing from the visible to long wave infrared (0.4 to 14 µm). Leveraging photon mapping techniques utilized by the computer graphics community, a first-principles based elastic Light Detection and Ranging (LIDAR) model was incorporated into the passive radiometry framework so that the model calculates arbitrary, time-gated radiances reaching the sensor for both the atmospheric and topographicreturns. The active LIDAR module handles a wide variety of complicated scene geometries, a diverse set of surface and participating media optical characteristics, multiple bounce and multiple scattering effects, and a flexible suite of sensormodels. This paper will present the numerical approaches employed to predict sensor reaching radiances andcomparisons with analytically predicted results. Representative data sets generated by the DIRSIG model for a topographical LIDAR will be shown. Additionally, the results from phenomenological case studies including standard terrain topography, forest canopy penetration, and camouflaged hard targets will be presented

Elastic LADAR modeling for synthetic imaging applications

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model was developed to create synthetic images of remotely sensed scenes (Schott et al. 1999). It is a quantitative model based on first principles that calculates the radiance reaching the sensor from the visible region of the spectrum through to the long-wave. DIRSIG generates a very accurate representation of what a sensor would see by modeling all processes involved in the imaging chain. Currently, DIRSIG only models light from passive sources such as the sun, blackbody radiation due to the temperature of an object, and local incoherent illuminants. Active systems allow the user to tailor the illumination source for specific applications. Remote sensing Laser Detection and Ranging (LADAR) systems that use a laser as the active source have existed for almost 40 years (Fiocco and Smullin 1963). LADAR systems are used to locate the position of an object. Light Detection and Ranging (LIDAR) systems are used to derive the properties of an object, such as density or chemical composition. Recently, advances in tunable lasers and infrared detectors have allowed much more sophisticated and accurate work to be done, but a comprehensive spectral LADAR/LIDAR modelhas yet to be developed. To provide a tool to assist in LADAR/LIDAR development, this research incorporates a first-principle-based elastic LADAR/LIDAR model into DIRSIG. It calculates the spectral irradiance at the focal plane for both the atmospheric and topographic return, based on the system characteristics and the assumed atmosphere. The model accounts for the geometrical form factor, a measure of the overlap between the sensor and receiver field of view, in both the monostatic and bistatic cases. The model includes the effect of multiple bounces from topographical targets. Currently, only direct detection systems are modeled. Several sources of noise are extensively modeled, such as speckle from rough surfaces and atmospheric turbulence phase effects

Cylindrical quasi-cavity waveguide for static wide angle pattern projection

Beam deflection methods such as rotary mirrors and motorized turning optical heads suffer from a variety of electro-mechanical related problems which affect laser scanning performance. These include wobble, jitter, wear, windage and synchronization issues. A novel optical structure consisting of two concentric and cylindrical interfaces with unique optical coating properties for the static projection of a laser spot array over a wide angle is demonstrated. The resulting ray trajectory through the waveguide is modeled using linear equations. Spot size growth is modeled using previously defined ray transfer matrices for tilted optical elements. The model is validated by comparison with experimental spot size measurements for 20 transmitted beams. This novel form of spot projection can be used as the projection unit in optical sensing devices which range to multiple laser footprints

A note on the depth-from-defocus mechanism of jumping spiders

Jumping spiders are capable of estimating the distance to their prey relying only on the information from one of their main eyes. Recently, it has been shown that jumping spiders perform this estimation based on image defocus cues. In order to gain insight into the mechanisms involved in this blur-to-distance mapping as performed by the spider and to judge whether inspirations can be drawn from spider vision for depth-from-defocus computer vision algorithms, we constructed a three-dimensional (3D) model of the anterior median eye of the Metaphidippus aeneolus, a well studied species of jumping spider. We were able to study images of the environment as the spider would see them and to measure the performances of a well known depth-from-defocus algorithm on this dataset. We found that the algorithm performs best when using images that are averaged over the considerable thickness of the spider's receptor layers, thus pointing towards a possible functional role of the receptor thickness for the spider's depth estimation capabilities

Curb-intersection feature based Monte Carlo Localization on urban roads

One of the most prominent features on an urban road is the curb, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. The combination of curb and intersection features and their idiosyncrasies carry significant information about the urban road network that can be exploited to improve a vehicle's localization. This paper introduces a Monte Carlo Localization (MCL) method using the curb-intersection features on urban roads. We propose a novel idea of “Virtual LIDAR” to get the measurement models for these features. Under the MCL framework, above road observation is fused with odometry information, which is able to yield precise localization. We implement the system using a single tilted 2D LIDAR on our autonomous test bed and show robust performance in the presence of occlusion from other vehicles and pedestrians