RINS-W: Robust Inertial Navigation System on Wheels

Brossard, Martin; Barrau, Axel; Bonnabel, Silvère

doi:10.1109/iros40897.2019.8968593

Cited by 68 publications

(35 citation statements)

References 25 publications

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“…This section demonstrates how to derive the deterministic, discrete time (world-centric) dynamics through analytical integration of the continuous state (24) and bias (25) dynamics. The contact, landmark, and bias dynamics are simply gaussian noise.…”

Section: Switching Between Left and Right-invariant Errorsmentioning

confidence: 99%

Contact-aided invariant extended Kalman filtering for robot state estimation

Hartley

Ghaffari

Grizzle

et al. 2020

The International Journal of Robotics Research

220

156

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Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental and lighting conditions, or fusion of kinematic and contact data with measurements from an inertial measurement unit (IMU). In this work, we develop a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design. This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points. We show that the error dynamics follows a log-linear autonomous differential equation with several important consequences: (a) the observable state variables can be rendered convergent with a domain of attraction that is independent of the system's trajectory; (b) unlike the standard EKF, neither the linearized error dynamics nor the linearized observation model depend on the current state estimate, which (c) leads to improved convergence properties and (d) a local observability matrix that is consistent with the underlying nonlinear system. Furthermore, we demonstrate how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions. We compare the convergence of the proposed InEKF with the commonly used quaternion-based EKF though both simulations and experiments on a Cassie-series bipedal robot. Filter accuracy is analyzed using motion capture, while a LiDAR mapping experiment provides a practical use case. Overall, the developed contactaided InEKF provides better performance in comparison with the quaternion-based EKF as a result of exploiting symmetries present in system. changes in lighting as well as the operating environment. It is therefore beneficial to develop a low-level state estimator that fuses data only from proprioceptive sensors to form accurate high-frequency state estimates. This approach was taken by Bloesch et al. [15] when developing a quaternion-based extended Kalman filter (QEKF) that combines inertial, contact, and kinematic data to estimate the robot's base pose, velocity, and a number of contact states. In this article, we expand upon these ideas to develop an invariant extended Kalman filter (InEKF) that has improved convergence and consistency properties allowing for a more robust observer that is suitable for long-term autonomy.The theory of invariant observer design is based on the estimation error being invariant under the action of a matrix Lie group [1,20], which has recently led to the development of the InEKF 1 [18,8,10,11] with successful applications and promising results in simultaneous localization and mapping [8,86] and aided inertial navigation systems [4,5,8,83]. The invariance of the estimation error with respect to a Lie group action is referred to as a symmetry of the system [5]. Summarized briefly, Barrau and Bonnabel [10] showed that if the state is defined on a Lie group, and the dynamics satisfy a particular "group affin...

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Section: Switching Between Left and Right-invariant Errorsmentioning

confidence: 99%

Contact-aided invariant extended Kalman filtering for robot state estimation

Hartley

Ghaffari

Grizzle

et al. 2020

The International Journal of Robotics Research

220

156

View full text Add to dashboard Cite

show abstract

“…The major downside of this approach is that the accuracy of these models is highly dependent on the data used to train them. Proponents of the psuedo-measurement approach argue that the best way to incorporate learned models is by adding them as additional measurements to an existing navigation system (e.g., a Kalman filter) [38,49,52,53,55,58]. The benefit of this approach is that the existing navigation system is augmented rather than replaced; however, the challenge of this approach comes from determining the details of how the learned model(s) will be integrated into the existing system.…”

Section: Related Workmentioning

confidence: 99%

“…Other authors have proposed using windowed inertial signals for pose estimation, thus avoiding this particular issue. Windowed approaches can be broken into two categories: models that use Long Short Term Memory (LSTM) neural networks [ 49 , 50 , 51 , 52 ] and models that use Convolutional Neural Networks (CNN) [ 38 , 53 ].…”

Section: Related Workmentioning

confidence: 99%

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units

Cole

Bozkurt

Lobatón

2020

Sensors

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Disaster robotics is a growing field that is concerned with the design and development of robots for disaster response and disaster recovery. These robots assist first responders by performing tasks that are impractical or impossible for humans. Unfortunately, current disaster robots usually lack the maneuverability to efficiently traverse these areas, which often necessitate extreme navigational capabilities, such as centimeter-scale clearance. Recent work has shown that it is possible to control the locomotion of insects such as the Madagascar hissing cockroach (Gromphadorhina portentosa) through bioelectrical stimulation of their neuro-mechanical system. This provides access to a novel agent that can traverse areas that are inaccessible to traditional robots. In this paper, we present a data-driven inertial navigation system that is capable of localizing cockroaches in areas where GPS is not available. We pose the navigation problem as a two-point boundary-value problem where the goal is to reconstruct a cockroach’s trajectory between the starting and ending states, which are assumed to be known. We validated our technique using nine trials that were conducted in a circular arena using a biobotic agent equipped with a thorax-mounted, low-cost inertial measurement unit. Results show that we can achieve centimeter-level accuracy. This is accomplished by estimating the cockroach’s velocity—using regression models that have been trained to estimate the speed and heading from the inertial signals themselves—and solving an optimization problem so that the boundary-value constraints are satisfied.

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“…Inertial navigation systems have long leveraged virtual and pseudo-measurements from IMU signals, e.g. the widespread Zero velocity UPdaTe (ZUPT) [18]- [20], as covariance adaptation [21]. In parallel, deep learning and more generally machine learning are gaining much interest for inertial navigation [22,23].…”

Section: Introductionmentioning

confidence: 99%

AI-IMU Dead-Reckoning

Brossard

Barrau

Bonnabel

2020

IEEE Trans. Intell. Veh.

Self Cite

199

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In this paper we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stops in the extreme case of exteroceptive sensors failure. The key components of the method are the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter. The method is tested on the KITTI odometry dataset, and our dead-reckoning inertial method based only on the IMU accurately estimates 3D position, velocity, orientation of the vehicle and self-calibrates the IMU biases. We achieve on average a 1.10% translational error and the algorithm competes with top-ranked methods which, by contrast, use LiDAR or stereo vision. We make our implementation open-source at: https://github.com/mbrossar/ai-imu-drIndex Terms-localization, deep learning, invariant extended Kalman filter, KITTI dataset, inertial navigation, inertial measurement unit

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RINS-W: Robust Inertial Navigation System on Wheels

Cited by 68 publications

References 25 publications

Contact-aided invariant extended Kalman filtering for robot state estimation

Contact-aided invariant extended Kalman filtering for robot state estimation

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units

AI-IMU Dead-Reckoning

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