Publications

  1. Batch Measurement Error Covariance Estimation for Robust Localization

    Watson, R., Taylor, C., Leishman, R., Gross, J.

    Proceedings of the 31th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2018)

    The factor graph has become the standard framework for representing a plethora of robotic navigation problems. One primary reason for this adoption by the community is the fast and efficient inference that can be conducted over the graph when a unimodal Gaussian noise model is assumed. However, the unimodal Gaussian noise model assumption does not reflect reality in many situations, particularly measurements that may include gross outliers (e.g. feature tracking between images, place recognition, or GNSS multipath). To combat this issue, several methodologies have been proposed for conducting robust inference on factor graphs. These models work by reducing the contribution of constraints that do not adhere to the specified noise model by scaling the corresponding elements of the information matrix. A unifying assumption shared by the proposed robust graph inference algorithms is that the measurement noise model is known a-priori and that the specified noise model does not vary with time. In the situation where the measurement model is not fully known, rejecting the outliers can become far more difficult. To overcome this issue, a novel method is proposed that utilizes a non-parametric soft clustering algorithm to iteratively estimate the covariance matrix corresponding to the uncorrupted measurements. The estimated covariance is then used within the max-mixtures framework to mitigate the effect of false constraints. The proposed methodology provides robust optimization in the face of faulty measurements where little or no information is provided about the measurement uncertainty.


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  2. Design of an Autonomous Precision Pollination Robot

    Ohi, N., Lassak, K., Watson, R., Strader, J., Du, Y., Yang, C., Hedrick, G., Nguyen, J., Harper, S., Reynolds, D., Kilic, C., Hikes, J., Mills, S., Castle, C., Buzzo, B., Waterland, N., Gross, J., Yong-Lak, P., Li, X., Gu, Y.

    IEEE/RSJ International Conference on Intelligent Robots and System (IROS 2018)

    Precision robotic pollination systems can not only fill the gap of declining natural pollinators, but can also surpass them in efficiency and uniformity, helping to feed the fast-growing human population on Earth. This paper presents the design and ongoing development of an autonomous robot named "BrambleBee", which aims at pollinating bramble plants in a greenhouse environment. Partially inspired by the ecology and behavior of bees, BrambleBee employs state-of-the-art localization and mapping, visual perception, path planning, motion control, and manipulation techniques to create an efficient and robust autonomous pollination system.


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  3. A Rover and Drone Team for Subterranean Environments: System Design Overview.

    Watson, R., Ohi, N., Harper, S., Kilic, C., Yang, C., Hikes, J., De Petrillo, M., Strader, J., Hedrick, G., Nichols, H., Upton, E. , Kirk, C., Hendricks, K., Reynolds, D., Darr,, J., Bredu, J., Langnese, E. , Gu, Y., Gross, J.

    Robotics Science & Systems (RSS 2018) Workshop on Challenges and Opportunities for Resilient Collective Intelligence in Subterranean Environment

    This abstract provides a concept overview of a rover and drone team for the exploration of subterranean environments that is currently under development. Recently, significant advances have been made in the field of autonomous robotics. These advances span from high-level semantic scene understanding to low-level efficient optimization. Through the utilization of these advances, autonomous robotic platforms are starting to leave the research laboratories and beginning to permeate novel environments. One such example of a novel environment is a subterranean tunnel, which presents not only commercial applications (e.g., the utilization in tunnel infrastructure monitoring) but also safety critical applications (e.g., the fast and efficient response to a tunnel collapse). The safety critical applications in subterranean environments provide many novel challenges for the robotics community (e.g., robust navigation in highly dynamic environments in the case of a robotic first response to a cave collapse)..


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  4. Robust Navigation in GNSS Degraded Environment Using Graph Optimization

    Watson, R. and Gross, J.

    Proceedings of the 30th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
    Selected as Best Presentation in the 2017 GNSS+ Multisensor Navigation in Challenging Environments Session

    Robust navigation in urban environments has received a considerable amount of both academic and commercial interest over recent years. This is primarily due to large commercial organizations such as Google and Uber stepping into the autonomous navigation market. Most of this research has shied away from Global Navigation Satellite System (GNSS) based navigation. The aversion to utilizing GNSS data is due to the degraded nature of the data in an urban environment (e.g., multipath, poor satellite visibility). The degradation of the GNSS data in urban environments makes it such that traditional (GNSS) positioning methods ( e.g., extended Kalman filter, particle filters) perform poorly. However, recent advances in robust graph theoretic based sensor fusion methods, primarily applied to Simultaneous Localization and Mapping (SLAM) based robotic applications, can also be applied to GNSS data processing. This paper will utilize one such method known as Incremental Smoothing and Mapping (ISAM2) in conjunction several robust optimization techniques to evaluate their applicability to robust GNSS data processing. The goals of this study are two-fold. First, for GNSS applications, we will experimentally evaluate the effectiveness of robust optimization techniques within a graph theoretic estimation framework. Second, by releasing the software developed and data sets used for this study, we will introduce a new open-source front-end to the Georgia Tech Smoothing and Mapping (GTSAM) library for the purpose of integrating GNSS pseudorange observations.


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  5. Cataglyphis: An autonomous sample return rover

    Gu, Y., Ohi, N., Lassak, K., Strader, J., Kogan, L., Hypes, A., Harper, S., Boyi, H., Gramlich, M., Kavi, R., Watson, R. , Cheng, M., Gross, J.

    Journal of Field Robotics, July 2017

    This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth-specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under-actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.


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  6. Flight Data Assessment of Tightly-Coupled PPP/INS using Real-Time Products

    Watson, R. , Gross, J., Bar-Sever, Y., Bertiger, W., Haines, B.,

    IEEE Aerospace and Electronic Systems Magazine, July 2017

    We present an analysis of the positioning performance of tightly-coupled Precise Point Positioning inertial navigation using two long-baseline flight data sets that include data from a navigation-grade Inertial Measurement Unit. The benefits of integrating inertial navigation with Precise Point Positioning are evaluated when using various GPS orbit and clock products (i.e., broadcast, real-time, and final), and whenever different troposphere models are adopted. We show that the positioning performance of PPP/INS, when using orbit and clock products generated in real-time is at the same level of accuracy as PPP when using post-processed orbit and clock products. In addition, we show that significant benefits with respect to solution convergence are available with tight-INS, leading to a greater than 30% reduction in three-dimensional (3D) Root Mean Squared (RMS) positioning errors. For example, when using real-time orbit and clock products with tightly-coupled inertial navigation, the mean and standard deviation of the position errors with respect to ambiguity-fixed post-processed reference solutions are reduced from 19 cm and 28 cm, to 15 and 18 cm, respectively. Furthermore, when using inertial data, a 10 cm or greater reduction in the 3D RMS position error is shown to be independent of the quality of the a priori nominal troposphere and troposphere modeling approach adopted. Index Terms—Precise Point Positioning, Inertial Navigation, Tightly-Coupled Navigation, Real-Time PPP, Airborne GeodesyPrecise Point Positioning, Inertial Navigation, Tightly-Coupled Navigation, Real-Time PPP, Airborne Geodesy.


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  7. Precise Point Positioning Inertial Navigation Integration for Kinematic Airborne Applications

    Watson, R.

    Thesis, May 2017

    UAVs have the potential for autonomous airborne remote sensing applications that require rapid response to natural hazards (e.g. volcano eruptions, earthquakes). As these applications require very accurate positioning, tightly coupled Global Positioning System (GPS) Precise Point Positioning (PPP) Inertial Navigation Systems (INS) are an attractive method to perform real-time aircraft positioning. In particular, PPP can achieve a level of positioning accuracy that is similar to RealTime Kinematic (RTK) GPS, without the need of a relatively close GPS reference station. However, the PPP method is known to converge to accurate positioning estimate more slowly when compared to RTK, a drawback of PPP that is amplified whenever the receiver platform is faced with GPS challenged environments, such as poor satellite visibility and frequent phase breaks.

    This thesis presents the use of a simulation environment that characterizes the position estimation performance sensitivity of PPP/INS through a Monte Carlo analysis that is considered under various conditions: such as, the intensity of multipath errors, the number of phase breaks, the satellite geometry, the atmospheric conditions, the noise characteristics of the inertial sensor, and the accuracy of GPS orbit products. After the PPP/INS formulation was verified in a simulation environment, the INS formulation was incorporated into NASA JPL’s Real-Time GIPSY-x. This software was then verified using eight recorded flight data sets provided by the National Geodetic Survey (NGS), National Oceanic and Atmospheric Administration (NOAA) program called Gravity for the Redefinition of the American Vertical Datum (GRAV-D).



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  8. Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs

    Gross, J., Watson, R., D'Urso, S., Gu, Y.

    International Journal of Aerospace Engineering, January 2016

    An experimental analysis of Global Positioning System (GPS) flight data collected onboard a Small Unmanned Aerial Vehicle (SUAV) is conducted in order to demonstrate that postprocessed kinematic Precise Point Positioning (PPP) solutions with precisions approximately 6 cm 3D Residual Sum of Squares (RSOS) can be obtained on SUAVs that have short duration flights with limited observational periods (i.e., only approximately 5 minutes of data). This is a significant result for the UAV flight testing community because an important and relevant benefit of the PPP technique over traditional Differential GPS (DGPS) techniques, such as Real-Time Kinematic (RTK), is that there is no requirement for maintaining a short baseline separation to a differential GNSS reference station. Because SUAVs are an attractive platform for applications such as aerial surveying, precision agriculture, and remote sensing, this paper offers an experimental evaluation of kinematic PPP estimation strategies using SUAV platform data. In particular, an analysis is presented in which the position solutions that are obtained from postprocessing recorded UAV flight data with various PPP software and strategies are compared to solutions that were obtained using traditional double-differenced ambiguity fixed carrier-phase Differential GPS (CP-DGPS). This offers valuable insight to assist designers of SUAV navigation systems whose applications require precise positioning.



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  9. Performance Characterization of Tightly-Coupled GNSS Precise Point Positioning Inertial Navigation within a Simulation Environment

    Watson, R. Sivaneri, V., Gross, J.

    Proceedings of the 2016 AIAA Guidance Navigation and Control Conference, San Diego, California. January 2016

    UAVs have the potential for autonomous airborne remote sensing applications that require rapid response to natural hazards (e.g.. volcano eruptions, earthquakes). As these applications require very accurate positioning, such as airborne Synthetic Aperture Radar, tightly coupled Global Positioning System (GPS) Precise Point Positioning (PPP) Inertial Navigation Systems (INS) are an attractive method to perform real-time aircraft positioning. In particular, PPP can achieve a level of positioning accuracy that is similar to Real-Time Kinematic (RTK) GPS, without the need of a relatively close GPS reference station. However, the PPP method is known to converge to accurate positioning more slowly when compared to RTK, a drawback of PPP that is amplified whenever the receiver platform is faced with GPS challenged environments, such as poor satellite visibility and frequent phase breaks. Unfortunately, these challenging conditions occur more often when the platform being positioned is an aircraft that experiences abrupt changes in attitude. In this paper we present the use of a simulation environment to characterize the position estimation performance sensitivity of PPP/INS through a Monte Carlo analysis that is considered under various conditions, such as: the intensity of multipath errors, the number of phase breaks that occur in a flight, satellite geometry, atmospheric conditions, noise characteristics and grade of the inertial sensor, and accuracy of GPS orbit products.


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  10. Integration of Inertial Navigation into Real-Time GIPSY-X (RTGx)

    Gross, J., Watson, R. Sivaneri, V., Bar-Sever, Y., Bertiger, W., Haines, B.

    Proceedings of the 28th International Technical Meeting of the Satellite Division of the Institute of Navigation (2015 ION GNSS+)

    This paper details the integration of an Inertial Navigation System (INS) processing capability within JPL’s RTGx geodetic data analysis and navigation software, and provides a performance analysis with experimental flight data in order to validate the implementation. The RTGx software, when used in conjunction with JPL’s Global Differential GPS System (GDGPS), can be configured for real-time kinematic Precise Point Positioning (K-PPP) for centimeterlevel positioning accuracy. Since 2006, RTGx’s predecessor, RTG, has provided operational real-time K-PPP for NASA’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) repeat pass interferometry mission on campaigns is meeting mission requirements during nominal science attitude or high banking turns, which induce signal loss-oflock, carrier-phase breaks and/or cycle slips. During these position solution’s sensitivity to phase breaks and slow convergence after loss-of-lock become apparent, and may impact tightly-coupled INS has been integrated into RTGx to offer additional robustness. This paper discusses the adopted the community by the National Geodetic Survey’s Kinematic performance evaluation. The integration of INS in terms of accuracy and precision with respect to a post- processed ambiguity-fixed reference solution. Furthermore, the integration of INS into the RTGx software will enable RTGx to support new application domains.


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