130 research outputs found
NeBula: TEAM CoSTAR’s robotic autonomy solution that won phase II of DARPA subterranean challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR’s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version
A factorization approach to inertial affine structure from motion
We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives
Present and Future of SLAM in Extreme Underground Environments
This paper reports on the state of the art in underground SLAM by discussing
different SLAM strategies and results across six teams that participated in the
three-year-long SubT competition. In particular, the paper has four main goals.
First, we review the algorithms, architectures, and systems adopted by the
teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to
approach for virtually all teams in the competition), heterogeneous multi-robot
operation (including both aerial and ground robots), and real-world underground
operation (from the presence of obscurants to the need to handle tight
computational constraints). We do not shy away from discussing the dirty
details behind the different SubT SLAM systems, which are often omitted from
technical papers. Second, we discuss the maturity of the field by highlighting
what is possible with the current SLAM systems and what we believe is within
reach with some good systems engineering. Third, we outline what we believe are
fundamental open problems, that are likely to require further research to break
through. Finally, we provide a list of open-source SLAM implementations and
datasets that have been produced during the SubT challenge and related efforts,
and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE
Transactions on Robotics for pre-approva
Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs
The factor graph framework is a convenient modeling technique for robotic
state estimation where states are represented as nodes, and measurements are
modeled as factors. When designing a sensor fusion framework for legged robots,
one often has access to visual, inertial, joint encoder, and contact sensors.
While visual-inertial odometry has been studied extensively in this framework,
the addition of a preintegrated contact factor for legged robots has been only
recently proposed. This allowed for integration of encoder and contact
measurements into existing factor graphs, however, new nodes had to be added to
the graph every time contact was made or broken. In this work, to cope with the
problem of switching contact frames, we propose a hybrid contact preintegration
theory that allows contact information to be integrated through an arbitrary
number of contact switches. The proposed hybrid modeling approach reduces the
number of required variables in the nonlinear optimization problem by only
requiring new states to be added alongside camera or selected keyframes. This
method is evaluated using real experimental data collected from a Cassie-series
robot where the trajectory of the robot produced by a motion capture system is
used as a proxy for ground truth. The evaluation shows that inclusion of the
proposed preintegrated hybrid contact factor alongside visual-inertial
navigation systems improves estimation accuracy as well as robustness to vision
failure, while its generalization makes it more accessible for legged
platforms.Comment: Detailed derivations are provided in the supplementary material
document listed under "Ancillary files
Heterogeneous Sensor Fusion for Accurate State Estimation of Dynamic Legged Robots
In this paper we present a system for the state
estimation of a dynamically walking and trotting quadruped.
The approach fuses four heterogeneous sensor sources (inertial,
kinematic, stereo vision and LIDAR) to maintain an accurate and
consistent estimate of the robot’s base link velocity and position
in the presence of disturbances such as slips and missteps. We
demonstrate the performance of our system, which is robust to
changes in the structure and lighting of the environment, as well
as the terrain over which the robot crosses. Our approach builds
upon a modular inertial-driven Extended Kalman Filter which
incorporates a rugged, probabilistic leg odometry component
with additional inputs from stereo visual odometry and LIDAR
registration. The simultaneous use of both stereo vision and
LIDAR helps combat operational issues which occur in real
applications. To the best of our knowledge, this paper is the first
to discuss the complexity of consistent estimation of pose and velocity
states, as well as the fusion of multiple exteroceptive signal
sources at largely different frequencies and latencies, in a manner
which is acceptable for a quadruped’s feedback controller. A
substantial experimental evaluation demonstrates the robustness
and accuracy of our system, achieving continuously accurate
localization and drift per distance traveled below 1 cm/m
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