481 research outputs found
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
Robust Legged Robot State Estimation Using Factor Graph Optimization
Legged robots, specifically quadrupeds, are becoming increasingly attractive
for industrial applications such as inspection. However, to leave the
laboratory and to become useful to an end user requires reliability in harsh
conditions. From the perspective of state estimation, it is essential to be
able to accurately estimate the robot's state despite challenges such as uneven
or slippery terrain, textureless and reflective scenes, as well as dynamic
camera occlusions. We are motivated to reduce the dependency on foot contact
classifications, which fail when slipping, and to reduce position drift during
dynamic motions such as trotting. To this end, we present a factor graph
optimization method for state estimation which tightly fuses and smooths
inertial navigation, leg odometry and visual odometry. The effectiveness of the
approach is demonstrated using the ANYmal quadruped robot navigating in a
realistic outdoor industrial environment. This experiment included trotting,
walking, crossing obstacles and ascending a staircase. The proposed approach
decreased the relative position error by up to 55% and absolute position error
by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs
Legged robots can outperform wheeled machines for most navigation tasks
across unknown and rough terrains. For such tasks, visual feedback is a
fundamental asset to provide robots with terrain-awareness. However, robust
dynamic locomotion on difficult terrains with real-time performance guarantees
remains a challenge. We present here a real-time, dynamic foothold adaptation
strategy based on visual feedback. Our method adjusts the landing position of
the feet in a fully reactive manner, using only on-board computers and sensors.
The correction is computed and executed continuously along the swing phase
trajectory of each leg. To efficiently adapt the landing position, we implement
a self-supervised foothold classifier based on a Convolutional Neural Network
(CNN). Our method results in an up to 200 times faster computation with respect
to the full-blown heuristics. Our goal is to react to visual stimuli from the
environment, bridging the gap between blind reactive locomotion and purely
vision-based planning strategies. We assess the performance of our method on
the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
foothold adaptation is clearly demonstrated by the overall robot behavior.Comment: 9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 201
Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation
This paper derives a contact-aided inertial navigation observer for a 3D
bipedal robot using the theory of invariant observer design. Aided inertial
navigation is fundamentally a nonlinear observer design problem; thus, current
solutions are based on approximations of the system dynamics, such as an
Extended Kalman Filter (EKF), which uses a system's Jacobian linearization
along the current best estimate of its trajectory. On the basis of the theory
of invariant observer design by Barrau and Bonnabel, and in particular, the
Invariant EKF (InEKF), we show that the error dynamics of the point
contact-inertial system follows a log-linear autonomous differential equation;
hence, the observable state variables can be rendered convergent with a domain
of attraction that is independent of the system's trajectory. Due to the
log-linear form of the error dynamics, it is not necessary to perform a
nonlinear observability analysis to show that when using an Inertial
Measurement Unit (IMU) and contact sensors, the absolute position of the robot
and a rotation about the gravity vector (yaw) are unobservable. We further
augment the state of the developed InEKF with IMU biases, as the online
estimation of these parameters has a crucial impact on system performance. We
evaluate the convergence of the proposed system with the commonly used
quaternion-based EKF observer using a Monte-Carlo simulation. In addition, our
experimental evaluation using a Cassie-series bipedal robot shows that the
contact-aided InEKF provides better performance in comparison with the
quaternion-based EKF as a result of exploiting symmetries present in the system
dynamics.Comment: Published in the proceedings of Robotics: Science and Systems 201
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
Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion
Implementing dynamic locomotion behaviors on legged robots requires a
high-quality state estimation module. Especially when the motion includes
flight phases, state-of-the-art approaches fail to produce reliable estimation
of the robot posture, in particular base height. In this paper, we propose a
novel approach for combining visual-inertial odometry (VIO) with leg odometry
in an extended Kalman filter (EKF) based state estimator. The VIO module uses a
stereo camera and IMU to yield low-drift 3D position and yaw orientation and
drift-free pitch and roll orientation of the robot base link in the inertial
frame. However, these values have a considerable amount of latency due to image
processing and optimization, while the rate of update is quite low which is not
suitable for low-level control. To reduce the latency, we predict the VIO state
estimate at the rate of the IMU measurements of the VIO sensor. The EKF module
uses the base pose and linear velocity predicted by VIO, fuses them further
with a second high-rate IMU and leg odometry measurements, and produces robot
state estimates with a high frequency and small latency suitable for control.
We integrate this lightweight estimation framework with a nonlinear model
predictive controller and show successful implementation of a set of agile
locomotion behaviors, including trotting and jumping at varying horizontal
speeds, on a torque-controlled quadruped robot.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA), 202
LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time
A reliable odometry source is a prerequisite to enable complex autonomy
behaviour in next-generation robots operating in extreme environments. In this
work, we present a high-precision lidar odometry system to achieve robust and
real-time operation under challenging perceptual conditions. LOCUS (Lidar
Odometry for Consistent operation in Uncertain Settings), provides an accurate
multi-stage scan matching unit equipped with an health-aware sensor integration
module for seamless fusion of additional sensing modalities. We evaluate the
performance of the proposed system against state-of-the-art techniques in
perceptually challenging environments, and demonstrate top-class localization
accuracy along with substantial improvements in robustness to sensor failures.
We then demonstrate real-time performance of LOCUS on various types of robotic
mobility platforms involved in the autonomous exploration of the Satsop power
plant in Elma, WA where the proposed system was a key element of the CoSTAR
team's solution that won first place in the Urban Circuit of the DARPA
Subterranean Challenge.Comment: Accepted for publication at IEEE Robotics and Automation Letters,
202
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