Search NASA⌕ Search

SEARCH · Search NASA

Results for “vision stereo autonomous navigation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

26 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Mobile Autonomous Humanoid Assistant

A mobile autonomous humanoid robot is assisting human co-workers at the Johnson Space Center with tool handling tasks. This robot combines the upper body of the National Aeronautics and Space Administration (NASA)/Defense Advanced Research Projects Agency (DARPA) Robonaut system with a Segway(TradeMark) Robotic Mobility Platform yielding a dexterous, maneuverable humanoid perfect for aiding human co-workers in a range of environments. This system uses stereo vision to locate human team mates and tools and a navigation system that uses laser range and vision data to follow humans while avoiding obstacles. Tactile sensors provide information to grasping algorithms for efficient tool exchanges. The autonomous architecture utilizes these pre-programmed skills to form human assistant behaviors. The initial behavior demonstrates a robust capability to assist a human by acquiring a tool from a remotely located individual and then following the human in a cluttered environment with the tool for future use.

Diftler, M. A.↗
NASA NTRS1990

Autonomous navigation and control of a Mars rover

A Mars rover will need to be able to navigate autonomously kilometers at a time. This paper outlines the sensing, perception, planning, and execution monitoring systems that are currently being designed for the rover. The sensing is based around stereo vision. The interpretation of the images use a registration of the depth map with a global height map provided by an orbiting spacecraft. Safe, low energy paths are then planned through the map, and expectations of what the rover's articulation sensors should sense are generated. These expectations are then used to ensure that the planned path is correctly being executed.

Miller, D. P.↗
NASA NTRS2010

Evaluating the Performance of Unmanned Ground Vehicle Water Detection

Water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation over cross-country terrain. During the Robotics Collaborative Technology Alliances (RCTA) program, the Jet Propulsion Laboratory (JPL) developed a set of water detection algorithms that are used to detect, localize, and avoid water bodies large enough to be a hazard to a UGV. The JPL water detection software performs the detection and localization stages using a forward-looking stereo pair of color cameras. The 3D coordinates of water body surface points are then output to a UGV's autonomous mobility system, which is responsible for planning and executing safe paths. There are three primary methods for evaluating the performance of the water detection software. Evaluations can be performed in image space on the intermediate detection product, in map space on the final localized product, or during autonomous navigation to characterize the avoidance of a variety of water bodies. This paper describes a methodology for performing the first two types of water detection performance evaluations.

stereo vision↗
NASA NTRS2002

Bioinspired engineering of exploration systems for NASA and DoD

A new approach called bioinspired engineering of exploration systems (BEES) and its value for solving pressing NASA and DoD needs are described. Insects (for example honeybees and dragonflies) cope remarkably well with their world, despite possessing a brain containing less than 0.01% as many neurons as the human brain. Although most insects have immobile eyes with fixed focus optics and lack stereo vision, they use a number of ingenious, computationally simple strategies for perceiving their world in three dimensions and navigating successfully within it. We are distilling selected insect-inspired strategies to obtain novel solutions for navigation, hazard avoidance, altitude hold, stable flight, terrain following, and gentle deployment of payload. Such functionality provides potential solutions for future autonomous robotic space and planetary explorers. A BEES approach to developing lightweight low-power autonomous flight systems should be useful for flight control of such biomorphic flyers for both NASA and DoD needs. Recent biological studies of mammalian retinas confirm that representations of multiple features of the visual world are systematically parsed and processed in parallel. Features are mapped to a stack of cellular strata within the retina. Each of these representations can be efficiently modeled in semiconductor cellular nonlinear network (CNN) chips. We describe recent breakthroughs in exploring the feasibility of the unique blending of insect strategies of navigation with mammalian visual search, pattern recognition, and image understanding into hybrid biomorphic flyers for future planetary and terrestrial applications. We describe a few future mission scenarios for Mars exploration, uniquely enabled by these newly developed biomorphic flyers.

Space Flight↗
NASA NTRSDate not supplied

Autonomous Nanosatellite Swarming (ANS) Using Radio Frequency and Optical Navigation

The Autonomous Nanosatellite Swarming (ANS) subsystem is a dynamics, guidance, navigation, and control software unit based on commercial-off-the-shelf avionics, which is distributed and deployed on a cluster of cooperative nanosatellites to enable future exploration missions to near-Earth objects (NEOs). ANS allows multiple nanosatellites to act together as a single large spacecraft through radio-frequency (RF) cross-links, optical navigation, and low-thrust control. In particular, ANS-equipped nanosatellites can utilize images of a NEO taken from multiple viewpoints to achieve stereo-vision capabilities and characterize the target body’s shape, gravity field, and dynamical properties. All this is enabled by the fusion of major advances to the state of the art in the astrodynamics of relative motion, RF and vision-based navigation, as well as low-thrust control.

Simone D'Amico↗
NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

TIR cameras can be used for day/night Unmanned Ground Vehicle (UGV) autonomous navigation when stealth is required. The quality of uncooled TIR cameras has significantly improved over the last decade, making them a viable option at low speed Limiting factors for stereo ranging with uncooled LWIR cameras are image blur and low texture scenes TIR perception capabilities JPL has explored includes: (1) single and dual band TIR terrain classification (2) obstacle detection (pedestrian, vehicle, tree trunks, ditches, and water) (3) perception thru obscurants

calibration↗
NASA NTRS2010

Daytime Water Detection Based on Color Variation

Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies (such as ponds). At far range, reflections of the sky provide a strong cue for water. But at close range, the color coming out of a water body dominates sky reflections and the water cue from sky reflections is of marginal use. We model this behavior by using water body intensity data from multiple frames of RGB imagery to estimate the total reflection coefficient contribution from surface reflections and the combination of all other factors. Then we describe an algorithm that uses one of the color cameras in a forward- looking, UGV-mounted stereo-vision perception system to detect water bodies in wide open areas. This detector exploits the knowledge that the change in saturation-to-brightness ratio across a water body from the leading to trailing edge is uniform and distinct from other terrain types. In test sequences approaching a pond under clear, overcast, and cloudy sky conditions, the true positive and false negative water detection rates were (95.76%, 96.71%, 98.77%) and (0.45%, 0.60%, 0.62%), respectively. This software has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA.

reflection coefficient↗
NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5?m) or long-wave infrared (LWIR) radiation (8-12?m). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.

terrain classification↗