Flydar: Magnetometer-based High Angular Rate Estimation during Gyro Saturation for SLAM

Tan, Chee How; Shaiful, Danial Sufiyan bin; Tang, Emmanuel; Khaw, Jien-Yi; Soh, Gim Song; Foong, Shaohui

doi:10.1109/icra40945.2020.9197486

Cited by 7 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An accelerometer mounted with sensor frame origin O s , which coincides with the robot's centre-of-mass O b , measures s a meas . It measures the robot's specific acceleration as defined in (16).…”

Section: B Estimationmentioning

confidence: 99%

“…The input lidar scan to the SLAM algorithm was compensated for the Flydar attitude. The pose estimates were compared to that using pure 2D SLAM in prior work [16]. Pure 2D SLAM assumes that the Flydar's motion is mostly planar and parallel to the ground frame.…”

Section: Slam Performance Evaluation Using Flydarmentioning

confidence: 99%

“…These include "nodding" a 2D laser scanner to scan in 3D representation [13], installing onboard mirrors for beam redirection away from the planar lidar's scanning plane for measuring altitude [10], [11], [14], and optimizing a sparse array of unidirectional lidar rangefinders for more accurate localization in known environments [15]. The Flydar, briefly introduced in our previous contributions [16], affords laser scanning using a single unidirectional laser.…”

Section: Introductionmentioning

confidence: 99%

“…Additional sensing and perception payload added for autonomy needs to be minimal to maintain its existing small form factor. The first work towards achieving selfautonomy was our previous contribution [16]. We presented the Flydar platform, which innovatively integrates a single laser with the robot's rotating locomotion to continuously actuate the laser for planar scanning [16].…”

Section: Introductionmentioning

confidence: 99%

“…The first work towards achieving selfautonomy was our previous contribution [16]. We presented the Flydar platform, which innovatively integrates a single laser with the robot's rotating locomotion to continuously actuate the laser for planar scanning [16]. However, the work mainly focused on estimating the high angular rate of the rotating platform to output accurate scans.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Flydar: A Passive Scanning Flying Lidar Sensing System for SLAM Using a Single Laser

et al. 2022

Self Cite

View full text Add to dashboard Cite

This paper presents the Flying Lidar (Flydar) aerial robot for simultaneous localization and mapping (SLAM). The Flydar integrates a single laser and capitalizes on the rotating dynamics of a nature-inspired aerial robot for omnidirectional scanning. This paper presents a 2.5D SLAM approach using the Flydar and does not assume that the Flydar's hovering plane, and thus the laser scan, is parallel to the ground plane, which is a key assumption in our previous work. A quaternion-based filter performs active correction of the Flydar's laser scan due to its transient attitude. A dual-accelerometer approach was incorporated to estimate the Flydar's scan rate during high angular rates flight beyond the gyroscope dynamic range. The proposed attitude filter output was experimentally evaluated statically on a bench-top setup and dynamically in flight, with an rms inclination estimate error of up to 1.1 o and 0.38 o respectively. The attitudecorrected lidar scan was used to estimate the robot pose for 2.5D SLAM. The 2.5D SLAM was experimentally validated on the Flydar and demonstrated to be superior to pure 2D SLAM in loop closure, position estimate drift, and rms error. Significantly, the 2.5D SLAM using the Flydar reports a low rms Euclidean error of up to 0.083 m, which is a 32.0% error reduction compared with our previous work, which uses 2D SLAM.

show abstract

Section: B Estimationmentioning

confidence: 99%

Section: Slam Performance Evaluation Using Flydarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Flydar: A Passive Scanning Flying Lidar Sensing System for SLAM Using a Single Laser

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

An Aerial Robot Achieving Bimodal Flight and Independent Attitude Control through Passive Morphing

Xu,

Chirarattananon

2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

Enhancing the adaptability and versatility of unmanned micro aerial vehicles (MAVs) is crucial for expanding their application range. In this article, a bimodal reconfigurable robot capable of operating in both regular quadcopter flight mode and a unique revolving flight mode is presented, which allows independent control of the vehicle's position and roll‐pitch attitude. This design incorporates passive revolute joints that enable seamless mid‐air transitions between the two modes, facilitated by centrifugal forces, without the need for additional actuators. In the regular mode, the robot operates with four vertical propellers, similar to conventional designs. In the revolving mode, the configuration shifts to two horizontal and two vertical propellers, achieving five degrees of freedom control with four actuators. This underactuated flight capability is made possible by exploiting the robot's fast revolving motion and the resulting cycle‐averaged forces and torques. Experimental validations confirm the feasibility and enhance the maneuverability of this design. The revolving flight mode provides significant advantages in applications such as mapping and confined space navigation.

show abstract

Finite time observer‐based output feedback control of MEMS gyroscopes with input saturation

Zhang

Shi

2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

The finite time observer‐based output feedback control of MEMS gyroscopes with input saturation is addressed in this article. For the unmeasured speed signal, neural network (NN) learning‐based finite time observer is designed to observe the speed signal in finite time, where the adaptive NN is employed to approximate the nonlinear dynamics with system uncertainties. Considering the input saturation, an auxiliary system is introduced to design the anti‐saturation compensator, such that the actual controller can escape saturation with less time and faster convergence can be achieved. Then the anti‐saturation compensator further works together with output feedback controller to control the dynamics of MEMS gyroscopes. Simulation results are given to verify the faster speed observation and faster convergence of the proposed controller under input saturation.

show abstract

Flydar: Magnetometer-based High Angular Rate Estimation during Gyro Saturation for SLAM

Cited by 7 publications

References 12 publications

Flydar: A Passive Scanning Flying Lidar Sensing System for SLAM Using a Single Laser

Flydar: A Passive Scanning Flying Lidar Sensing System for SLAM Using a Single Laser

An Aerial Robot Achieving Bimodal Flight and Independent Attitude Control through Passive Morphing

Finite time observer‐based output feedback control of MEMS gyroscopes with input saturation

Contact Info

Product

Resources

About