Acknowledgements 1. Introduction 2. Electromagnetic wave propagation 3. The absorption of light 4. Specular reflection 5. Single particle scattering: perfect spheres 6. Single particle scattering: irregular particles 7. Propagation in a nonuniform medium: the equation of radiative transfer 8. The bidirectional reflectance of a semi-infinite medium 9. The opposition effect 10. A miscellany of bidirectional reflectances and related quantities 11. Integrated reflectances and planetary photometry 12. Photometric effects of large scale roughness 13. Polarization 14. Reflectance spectroscopy 15. Thermal emission and emittance spectroscopy 16. Simultaneous transport of energy by radiation and conduction Appendix A. A brief review of vector calculus Appendix B. Functions of a complex variable Appendix C. The wave equation in spherical coordinates Appendix D. Fraunhoffer diffraction by a circular hole Appendix E. Table of symbols Bibliography Index.

Theory of Reflectance and Emittance Spectroscopy

Recently, there has been growing interest in developing unmanned aircraft systems (UAS) with advanced onboard autonomous capabilities. This paper describes the current state of the art in autonomous rotorcraft UAS (RUAS) and provides a detailed literature review of the last two decades of active research on RUAS. Three functional technology areas are identified as the core components of an autonomous RUAS. Guidance, navigation, and control (GNC) have received much attention from the research community, and have dominated the UAS literature from the nineties until now. This paper first presents the main research groups involved in the development of GNC systems for RUAS. Then it describes the development of a framework that provides standard definitions and metrics characterizing and measuring the autonomy level of a RUAS using GNC aspects. This framework is intended to facilitate the understanding and the organization of this survey paper, but it can also serve as a common reference for the UAS community. The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests. These algorithms and systems have been classified into different categories and classes based on the autonomy level they provide and the algorithmic approach used. Finally, the paper discusses the RUAS literature in general and highlights challenges that need to be addressed in developing autonomous systems for unmanned rotorcraft. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

1. Introduction 2. Radial velocities 3. Astrometry 4. Timing 5. Microlensing 6. Transits 7. Imaging 8. Host stars 9. Brown dwarfs and free-floating planets 10. Formation and evolution 11. Interiors and atmospheres 12. The Solar System Appendixes References Index.

/pdf/the-exoplanet-handbook-3g3nzlvc20.pdf

The Exoplanet Handbook

Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.

A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation

In this paper, we present the vision-aided inertial navigation (VISINAV) algorithm that enables precision planetary landing. The vision front-end of the VISINAV system extracts 2-D-to-3-D correspondences between descent images and a surface map (mapped landmarks), as well as 2-D-to-2-D feature tracks through a sequence of descent images (opportunistic features). An extended Kalman filter (EKF) tightly integrates both types of visual feature observations with measurements from an inertial measurement unit. The filter computes accurate estimates of the lander's terrain-relative position, attitude, and velocity, in a resource-adaptive and hence real-time capable fashion. In addition to the technical analysis of the algorithm, the paper presents validation results from a sounding-rocket test flight, showing estimation errors of only 0.16 m/s for velocity and 6.4 m for position at touchdown. These results vastly improve current state of the art for terminal descent navigation without visual updates, and meet the requirements of future planetary exploration missions.

/pdf/vision-aided-inertial-navigation-for-spacecraft-entry-16y1solbjk.pdf

Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing

We report the following results from a decade of infrared radiometry of Io: (1) The average global heat flow is more than approx. 2.5 W/sq.m, (2) large warm (less than or equal to 200 K) volcanic regions dominate the global heat flow, (3) smal high-temperature (greater than or = 300 K) 'hotspots' contribute little to the average heat flow, (4) thermal anomalies on the leading hemisphere contribute about half of the heat flow, (5) a substantial amount of heat is radiated during Io's night, (6) high-temperature (greater than or = 600 K) 'outbursts' occurred during approx. 4% of the nights we observed, (7) 'Loki' is the brightest, persistent, infrared emission feature, and (8) some excess emission is always present at the longitude of Loki, but its intensity and other characteristics change between apparitions. Observations of Io at M(4.8 micrometer), 8.7 micrometer, N(10 micrometer), and Q(20 micrometer) with the Infrared Telescope Facility presented here were collected during nine apparitions between 1983 and 1993. These measurements provide full longitudinal coveraged as well as an eclipse observation and the detection of two outbursts. Reflected sunlight, passive thermal emission, and radiation from thermal anomalies all contribute to the observed flux densities. We find that a new thermophysical model is required to match all the data. Two key elements of this model are (1) a 'thermal reservoir' unit which lowers daytime temperatures, and (2) the 'thermal pedestal effect' which shifts to shorter wavelengths the spectral emission due to the reradiation of solar energy absorbed by the thermal anomalies. The thermal anomalies are modeled with a total of 10 source components at five locations. Io's heat flow is the sum of the power from these components.

Io's heat flow from infrared radiometry: 1983–1993

Information available on the photometric and polarimetric properties of Mercury's surface is discussed, with special consideration given to data concerning the nature of the Mercurial regolith and the comparison of the Mercury's photometric properties with those of the moon. The results from an analysis performed in terms of Hapke's (1981, 1984, 1986) photometric function confirm the remarkable similarity of photometric properties of the two celestial bodies. However, their highland/mare alabedo ratios differs considerably; while the ratio is about 2 on the moon, it is only about 1.4 on Mercury. Mercury also shows slightly lower values of the maximum positive polarization observed, a difference that is consistent with either a slightly higher average reflectance for Mercury, or with a difference in regolith texture, or both.

Photometry and polarimetry of Mercury

During the Mars Exploration Rover (MER) landings, the Descent Image Motion Estimation System (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combined measurements from a descent camera, a radar altimeter, and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm used altitude and attitude measurements to rectify images to a level ground plane. Feature selection and tracking were employed in the rectified images to compute the horizontal motion between images. Differences of consecutive motion estimates were then compared to inertial measurements to verify correct feature tracking. DIMES combined sensor data from multiple sources in a novel way to create a low-cost, robust, and computationally efficient velocity estimation solution, and DIMES was the first robotics vision system used to control a spacecraft during planetary landing. This paper presents the design and implementation of the DIMES algorithm, the assessment of the algorithm performance using a high fidelity Monte Carlo simulation, validation of performance using field test data and the detailed results from the two landings on Mars.

DIMES was used successfully during both MER landings. In the case of Spirit, had DIMES not been used onboard, the total velocity would have been at the limits of the airbag capability. Fortunately, DIMES computed the actual steady state horizontal velocity and it was used by the thruster firing logic to reduce the total velocity prior to landing. For Opportunity, DIMES computed the correct velocity, and the velocity was small enough that the lander performed no action to remove it.

https://www-robotics.jpl.nasa.gov/publications/Reg_Willson/DIMES_IJCV_20070117.pdf

Design Through Operation of an Image-Based Velocity Estimation System for Mars Landing

The Mars Exploration Rover (MER) Descent Image Motion Estimation System (DIMES) is the first autonomous machine vision system used to safely land a robotics payload on another planet. DIMES consists of a descent camera and an algorithm for estimating horizontal velocity using image, inertial and altitude measurements. Before DIMES was accepted by MER for inclusion in the mission, its performance was validated through field testing using a manned helicopter to image three Mars analog test sites. Statistical analysis of the resulting 1900+ test cases showed that DIMES met its velocity estimation requirement. This paper describes the DIMES field test approach and associated results.

http://www-robotics.jpl.nasa.gov/publications/Andrew_Johnson/DIMES_ICRA05_FINAL.pdf

Jay D. Goguen

Papers

Io's heat flow from infrared radiometry: 1983–1993

Photometry and polarimetry of Mercury

Design Through Operation of an Image-Based Velocity Estimation System for Mars Landing

Field Testing of the Mars Exploration Rovers Descent Image Motion Estimation System

Carbon dioxide segregation in 1:4 and 1:9 CO2:H2O ices