David R. Thompson

IJCAI Conference 2016 Conference Paper

Precision Instrument Targeting via Image Registration for the Mars 2020 Rover

• Gary Doran
• David R. Thompson
• Tara Estlin

A key component of Mars exploration is the operation of robotic instruments on the surface, such as those on board the Mars Exploration Rovers, the Mars Science Laboratory (MSL), and the planned Mars 2020 Rover. As the instruments carried by these rovers have become more advanced, the area targeted by some instruments becomes smaller, revealing more fine-grained details about the geology and chemistry of rocks on the surface. However, thermal fluctuations, rover settling or slipping, and inherent inaccuracies in pointing mechanisms all lead to pointing error that is on the order of the target size (several millimeters) or larger. We show that given a target located on a previously acquired image, the rover can align this with a new image to visually locate the target and refine the current pointing. Due to round-trip communication constraints, this visual targeting must be done efficiently on board the rover using relatively limited computing hardware. We employ existing ORB features for landmark-based image registration, describe and theoretically justify a novel approach to filtering false landmark matches, and employ a random forest classifier to automatically reject failed alignments. We demonstrate the efficacy of our approach using over 3, 800 images acquired by Remote Micro-Imager on board the Curiosity rover.

IS Journal 2014 Journal Article

Real-Time Adaptive Event Detection in Astronomical Data Streams

• David R. Thompson
• Sarah Burke-Spolaor
• Adam T. Deller
• Walid A. Majid
• Divya Palaniswamy
• Steven J. Tingay
• Kiri L. Wagstaff
• Randall B. Wayth

A new generation of observational science instruments is dramatically increasing collected data volumes in a range of fields. These instruments include the Square Kilometer Array (SKA), Large Synoptic Survey Telescope (LSST), terrestrial sensor networks, and NASA satellites participating in "decadal survey"' missions. Their unprecedented coverage and sensitivity will likely reveal wholly new categories of unexpected and transient events. Commensal methods passively analyze these data streams, recognizing anomalous events of scientific interest and reacting in real time. Here, the authors report on a case example: Very Long Baseline Array Fast Transients Experiment (V-FASTR), an ongoing commensal experiment at the Very Long Baseline Array (VLBA) that uses online adaptive pattern recognition to search for anomalous fast radio transients. V-FASTR triages a millisecond-resolution stream of data and promotes candidate anomalies for further offline analysis. It tunes detection parameters in real time, injecting synthetic events to continually retrain itself for optimum performance. This self-tuning approach retains sensitivity to weak signals while adapting to changing instrument configurations and noise conditions. The system has operated since July 2011, making it the longest-running real-time commensal radio transient experiment to date.

TIST Journal 2012 Journal Article

AEGIS Automated Science Targeting for the MER Opportunity Rover

• Tara A. Estlin
• Benjamin J. Bornstein
• Daniel M. Gaines
• Robert C. Anderson
• David R. Thompson
• Michael Burl
• Rebecca Castaño
• Michele Judd

The Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission’s Opportunity rover in December 2009 and has successfully demonstrated automated onboard targeting based on scientist-specified objectives. Prior to AEGIS, images were transmitted from the rover to the operations team on Earth; scientists manually analyzed the images, selected geological targets for the rover’s remote-sensing instruments, and then generated a command sequence to execute the new measurements. AEGIS represents a significant paradigm shift---by using onboard data analysis techniques, the AEGIS software uses scientist input to select high-quality science targets with no human in the loop. This approach allows the rover to autonomously select and sequence targeted observations in an opportunistic fashion, which is particularly applicable for narrow field-of-view instruments (such as the MER Mini-TES spectrometer, the MER Panoramic camera, and the 2011 Mars Science Laboratory (MSL) ChemCam spectrometer). This article provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.

TIST Journal 2012 Journal Article

Using Clustering and Metric Learning to Improve Science Return of Remote Sensed Imagery

• David S. Hayden
• Steve Chien
• David R. Thompson
• Rebecca Castaño

Current and proposed remote space missions, such as the proposed aerial exploration of Titan by an aerobot, often can collect more data than can be communicated back to Earth. Autonomous selective downlink algorithms can choose informative subsets of data to improve the science value of these bandwidth-limited transmissions. This requires statistical descriptors of the data that reflect very abstract and subtle distinctions in science content. We propose a metric learning strategy that teaches algorithms how best to cluster new data based on training examples supplied by domain scientists. We demonstrate that clustering informed by metric learning produces results that more closely match multiple scientists’ labelings of aerial data than do clusterings based on random or periodic sampling. A new metric-learning strategy accommodates training sets produced by multiple scientists with different and potentially inconsistent mission objectives. Our methods are fit for current spacecraft processors (e.g., RAD750) and would further benefit from more advanced spacecraft processor architectures, such as OPERA.

IJCAI Conference 2009 Conference Paper

• David R. Thompson

In this work, novelty detection identifies salient image features to guide autonomous robotic exploration. There is little advance knowledge of the features in the scene or the proportion that should count as outliers. A new algorithm addresses this ambiguity by modeling novel data in advance and characterizing regular data at run time. Detection thresholds adapt dynamically to reduce misclassi- fication risk while accommodating homogeneous and heterogeneous scenes. Experiments demonstrate the technique on a representative set of navigation images from the Mars Exploration Rover “Opportunity. ” An efficient image analysis procedure filters each image using the integral transform. Pixel-level features are aggregated into covariance descriptors that represent larger regions. Finally, a distance metric derived from generalized eigenvalues permits novelty detection with kernel density estimation. Results suggest that exploiting training examples of novel data can improve performance in this domain.

AAAI Conference 2007 Short Paper

Predictive Exploration for Autonomous Science

• David R. Thompson