A super-resolution algorithm for enhancement of FLASH LIDAR data

Abstract

A novel method for enhancement of the spatial resolution of 3-diminsional Flash Lidar images is being proposed for generation of elevation maps of terrain from a moving platform. NASA recognizes the Flash LIDAR technology as an important tool for enabling safe and precision landing in future unmanned and crewed lunar and planetary missions. The ability of the Flash LIDAR to generate 3-dimensional maps of the landing site area during the final stages of the descent phase for detection of hazardous terrain features such as craters, rocks, and steep slopes is under study in the frame of the Autonomous Landing and Hazard Avoidance (ALHAT) project. Since single frames of existing FLASH LIDAR systems are not sufficient to build a map of entire landing site with acceptable spatial resolution and precision, a super-resolution approach utilizing multiple frames has been developed to overcome the instrument's limitations. Performance of the super-resolution algorithm has been analyzed through a series of simulation runs obtained from a high fidelity Flash LIDAR model and a high resolution synthetic lunar elevation map. For each simulation run, a sequence of FLASH LIDAR frames are recorded and processed as the spacecraft descends toward the landing site. Simulations runs having different trajectory profiles and varying LIDAR look angles of the terrain are also analyzed. The results show that adequate levels of accuracy and precision are achieved for detecting hazardous terrain features and identifying safe areas of the landing site.

Development of pulsed‐laser three‐dimensional imaging flash lidar using APD arrays

“…To improve the imaging resolution, a three‐dimensional super‐resolution algorithm was implemented on the images obtained from the ALHAT project. The super‐resolution algorithm exhibited the ability to 4× digital magnification and can identify hazards like rocks and craters 100 …”

Probabilistic Hazard Detection for Autonomous Safe Landing

“…However, a number of HDA algorithms have been proposed in the 1990's and early 2000's. These algorithms considered using descent images from electrooptical (EO) passive sensors in monocular and stereo configurations, as well as 3-D point clouds form active scanning and flash Lidar sensors [2,5,16,22,30,34,35]. More recently, technologies for automated detection of rocks and craters from EO imagery were selected by NASA's New Millennium program but a funding shortfall precluded further development [18,31,32].…”

“…Civilian and military organizations that carry out such operations could, for example, quickly deploy a swarm of small unmanned Short take off and Landing (STOL) craft equipped with HDA and thus having the ability to autonomously choose a safe landing site as close as possible to the target area. An example of efforts in this direction, specifically aimed at unmanned autonomous helicopter landing capabilities, is underway sponsored by the Office of Naval Research (ONR) under the Autonomous Aerial Cargo/Utility Systems (AACUS) program [5].…”

Fusion of multi-scale DEMs using a regularized super-resolution method

“…Therefore, methods in the spatial domain have become more popular in recent years, including iterative back projection (IBP) (Irani and Peleg 1991), projection onto convex sets (POCS) (Zhang and Zhou 2011), and a group of regularized variational methods (Park et al 2003;Ng et al 2007b;Zhang et al 2007, Zeng andYang 2013). Among these methods, Bulyshev et al (2011) attempted to employ a back projection (BP) method for the processing of multiple three-dimensional Flash LiDAR DEM data. However, this method has only been tested on simulated data, ignoring the possible inconsistency between multi-sensor and multi-scale DEMs.…”