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Joint On-Manifold Gravity and Accelerometer Intrinsics Estimation for Inertially Aligned Mapping

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arXiv 2023年

作者： Nemiroff, Ryan Chen, Kenny Lopez, Brett T. The Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los AngelesCA United States

Aligning a robot’s trajectory or map to the inertial frame is a critical capability that is often difficult to do accurately even though inertial measurement units (IMUs) can observe absolute roll and pitch with respect to gravity. Accelerometer biases and scale factor errors from the IMU’s initial calibration are often the major source of inaccuracies when aligning the robot’s odometry frame with the inertial frame, especially for low-grade IMUs. Practically, one would simultaneously estimate the true gravity vector, accelerometer biases, and scale factor to improve measurement quality but these quantities are not observable unless the IMU is sufficiently excited. While several methods estimate accelerometer bias and gravity, they do not explicitly address the observability issue nor do they estimate scale factor. We present a fixed-lag factor-graph-based estimator to address both of these issues. In addition to estimating accelerometer scale factor, our method mitigates limited observability by optimizing over a time window an order of magnitude larger than existing methods with significantly lower computational burden. The proposed method, which estimates accelerometer intrinsics and gravity separately from the other states, is enabled by a novel, velocity-agnostic measurement model for intrinsics and gravity, as well as a new method for gravity vector optimization on S2. Accurate IMU state prediction, gravity-alignment, and roll/pitch drift correction are experimentally demonstrated on public and self-collected datasets in diverse environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Observability

A Contracting Hierarchical Observer for Pose-Inertial Fusion

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arXiv 2023年

作者： Lopez, Brett T. Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los AngelesCA United States

This work presents a contracting hierarchical observer that fuses position and orientation measurements with an IMU to generate smooth position, linear velocity, orientation, and IMU bias estimates that are guaranteed to converge to their true values. The proposed approach is composed of two contracting observers. The first is a quaternion-based orientation observer that also estimates gyroscope bias. The output of the orientation observer serves as an input for another contracting observer that estimates position, linear velocity, and accelerometer bias thus forming a hierarchy. We show that the proposed observer guarantees all state estimates converge to their true values. Simulation results confirm the theoretical performance guarantees. Copyright © 2023, The Authors. All rights reserved.

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Unmatched control Barrier Functions: Certainty Equivalence Adaptive Safety

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Unmatched Control Barrier Functions: Certainty Equivalence A...

American control Conference (ACC)

作者： Brett T. Lopez Jean-Jacques E. Slotine Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los Angeles CA Nonlinear Systems Laboratory Massachusetts Institute of Technology Cambridge MA

This work applies universal adaptive control to control barrier functions to achieve safe control of dynamical systems with parametric model uncertainties. The proposed approach utilizes the certainty equivalence principle to methodically select a model-parameterized control barrier function and corresponding safety controller from an allowable set with instantaneous parameter estimates. While such a combination does not necessarily yield forward invariance without additional requirements on the barrier function, we show that safety can indeed be established by simply adjusting the adaptation gain online. Simulation results demonstrate the approach.

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Dynamic Adaptation Gains for Nonlinear Systems with Unmatched Uncertainties

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arXiv 2023年

作者： Lopez, Brett T. Slotine, Jean-Jacques Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los AngelesCA United States Nonlinear Systems Laboratory Massachusetts Institute of Technology CambridgeMA United States

We present a new direct adaptive control approach for nonlinear systems with unmatched and matched uncertainties. The method relies on adjusting the adaptation gains of individual unmatched parameters whose adaptation transients would otherwise destabilize the closed-loop system. The approach also guarantees the restoration of the adaptation gains to their nominal values and can readily incorporate direct adaptation laws for matched uncertainties. The proposed framework is general as it only requires stabilizability for all possible models. Copyright © 2023, The Authors. All rights reserved.

关键词： Nonlinear systems

Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction

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Direct LiDAR-Inertial Odometry: Lightweight LIO with Continu...

IEEE International Conference on robotics and Automation (ICRA)

作者： Kenny Chen Ryan Nemiroff Brett T. Lopez Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles CA USA Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los Angeles CA USA Department of Mechanical and Aerospace Engineering University of California Los Angeles Los Angeles CA USA

Aggressive motions from agile flights or traversing irregular terrain induce motion distortion in LiDAR scans that can degrade state estimation and mapping. Some methods exist to mitigate this effect, but they are still too simplistic or computationally costly for resource-constrained mobile robots. To this end, this paper presents Direct LiDAR-Inertial Odometry (DLIO), a lightweight LiDAR-inertial odometry algorithm with a new coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction. The key to our method lies in the construction of a set of analytical equations which are parameterized solely by time, enabling fast and parallelizable point-wise deskewing. This method is feasible only because of the strong convergence properties in our nonlinear geometric observer, which provides provably correct state estimates for initializing the sensitive IMU integration step. Moreover, by simultaneously performing motion correction and prior generation, and by directly registering each scan to the map and bypassing scan-to-scan, DLIO's condensed architecture is nearly 20% more computationally efficient than the current state-of-the-art with a 12% increase in accuracy. We demonstrate DLIO's superior localization accuracy, map quality, and lower computational overhead as compared to four state-of-the-art algorithms through extensive tests using multiple public benchmark and self-collected datasets.

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Direct LiDAR-Inertial Odometry and Mapping: Perceptive and Connective SLAM

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arXiv 2023年

作者： Chen, Kenny Nemiroff, Ryan Lopez, Brett T. Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States The Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA United States The Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los AngelesCA United States

This paper presents Direct LiDAR-Inertial Odometry and Mapping (DLIOM), a robust SLAM algorithm with an explicit focus on computational efficiency, operational reliability, and real-world efficacy. DLIOM contains several key algorithmic innovations in both the front-end and back-end subsystems to design a resilient LiDAR-inertial architecture that is perceptive to the environment and produces accurate localization and high-fidelity 3D mapping for autonomous robotic platforms. Our ideas spawned after a deep investigation into modern LiDAR SLAM systems and their inabilities to generalize across different operating environments, in which we address several common algorithmic failure points by means of proactive safe-guards to provide long-term operational reliability in the unstructured real world. We detail several important innovations to localization accuracy and mapping resiliency distributed throughout a typical LiDAR SLAM pipeline to comprehensively increase algorithmic speed, accuracy, and robustness. In addition, we discuss insights gained from our ground-up approach while implementing such a complex system for real-time state estimation on resource-constrained systems, and we experimentally show the increased performance of our method as compared to the current state-of-the-art on both public benchmark and self-collected datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

Adaptive Variants of Optimal Feedback Policies

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arXiv 2021年

作者： Lopez, Brett T. Slotine, Jean-Jacques E. Verifiable and Control-Theoretic Robotics Laboratory University of California Los AngelesCA United States Nonlinear Systems Laboratory Massachusetts Institute of Technology MA United States Google AI United States

The stable combination of optimal feedback policies with online learning is studied in a new control-theoretic framework for uncertain nonlinear systems. The framework can be systematically used in transfer learning and sim-to-real applications, where an optimal policy learned for a nominal system needs to remain effective in the presence of significant variations in parameters. Given unknown parameters within a bounded range, the resulting adaptive control laws guarantee convergence of the closed-loop system to the state of zero cost. Online adjustment of the learning rate is used as a key stability mechanism, and preserves certainty equivalence when designing optimal policies without assuming uncertainty to be within the control range. The approach is illustrated on the familiar mountain car problem, where it yields near-optimal performance despite the presence of parametric model uncertainty. Copyright © 2021, The Authors. All rights reserved.

关键词： Adaptive control systems

Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction

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arXiv 2022年

作者： Chen, Kenny Nemiroff, Ryan Lopez, Brett T. The Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States The Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA United States The Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los AngelesCA United States

关键词： State estimation