Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

Choi, Christopher L.; Rebello, Jason; Koppe, Leonid; Ganti, Pranav; Das, Arun; Waslander, Steven L.

doi:10.1109/icra.2018.8462920

Cited by 6 publications

(3 citation statements)

References 29 publications

(49 reference statements)

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“…One of the most used mechanisms to define the orientation of cameras and other devices is the so-called gimbal. This mechanism is widely used in patents [5][6][7][8] and even in measuring large-scale systems such as buildings [9].…”

Section: Related Workmentioning

confidence: 99%

Orientation Modeling Using Quaternions and Rational Trigonometry

et al. 2022

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In recent years, the recreational and commercial use of flight and driving simulators has become more popular. All these applications require the calculation of orientation in either two or three dimensions. Besides the Euler angles notation, other alternatives to represent rigid body rotations include axis-angle notation, homogeneous transformation matrices, and quaternions. All these methods involve transcendental functions in their calculations, which represents a disadvantage when these algorithms are implemented in hardware. The use of transcendental functions in software-based algorithms may not represent a significant disadvantage, but in hardware-based algorithms, the potential of rational models stands out. Generally, to calculate transcendental functions in hardware, it is necessary to utilize algorithms based on the CORDIC algorithm, which requires a significant amount of hardware resources (parallel) or the design of a more complex control unit (pipelined). This research presents a new procedure for model orientation using rational trigonometry and quaternion notation, avoiding trigonometric functions for calculations. We describe the orientation of a gimbal mechanism presented in many applications, from autonomous vehicles such as cars or drones to industrial manipulators. This research aims to compare the efficiency of a rational implementation to classical modeling using the techniques mentioned above. Furthermore, we simulate the models with software tools and propose a hardware architecture to implement our algorithms.

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Section: Related Workmentioning

confidence: 99%

Orientation Modeling Using Quaternions and Rational Trigonometry

et al. 2022

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“…Our previous work, demonstrating the localisation performance of VT&R on fixed-wing UAVs [7] and integration of a gimballed camera on a ground vehicle [10], is the lead-up to this work. While there are few examples using a gimbaled camera on ground vehicles, a number of examples exist in demonstrations on UAVs [11]- [15]. This discrepency can most likely be attributed to the larger dynamic motions of UAVs, where the utility of a gimbal is highly justified to ensure smooth sensor motion.…”

Section: Previous Workmentioning

confidence: 99%

There's No Place Like Home: Visual Teach and Repeat for Emergency Return of Multirotor UAVs During GPS Failure

Warren

Greeff

Patel

et al. 2019

IEEE Robot. Autom. Lett.

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Redundant navigation systems are critical for safe operation of UAVs in high-risk environments. Since most commercial UAVs almost wholly rely on GPS, jamming, interference and multi-pathing are real concerns that usually limit their operations to low-risk environments and VLOS. This paper presents a vision-based route-following system for the autonomous, safe return of UAVs under primary navigation failure such as GPS jamming. Using a Visual Teach and Repeat framework to build a visual map of the environment during an outbound flight, we show the autonomous return of the UAV by visually localising the live view to this map when a simulated GPS failure occurs, controlling the vehicle to follow the safe outbound path back to the launch point. Using gimbal-stabilised stereo vision alone, without reliance on external infrastructure or inertial sensing, Visual Odometry and localisation are achieved at altitudes of 5-25 m and flight speeds up to 55 km/h. We examine the performance of the visual localisation algorithm under a variety of conditions and also demonstrate closed-loop autonomy along a complicated 450 m path.

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“…In [21], Choi et al calibrate the extrinsic parameters of a multi-camera system and the DH parameters of a pan-tilt unit (regarded as a 2-DoF robot manipulator) simultaneously. One of the camera is attached to the pan-tilt unit, and a fiducial marker is used to provide the reference frame for all the cameras.…”

Section: A Calibration Of Robot's Kinematic Parametersmentioning

confidence: 99%

SCALAR: Simultaneous Calibration of 2D Laser and Robot Kinematic Parameters Using Planarity and Distance Constraints

Lembono,

Suárez-Ruiz,

Pham

2019

Preprint

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In this paper, we propose SCALAR, a calibration method to simultaneously calibrate the kinematic parameters of a 6-DoF robot and the extrinsic parameters of a 2D Laser Range Finder (LRF) attached to the robot's flange. The calibration setup requires only a flat plate with two small holes carved on it at a known distance from each other, and a sharp tool-tip attached to the robot's flange. The calibration is formulated as a nonlinear optimization problem where the laser and the tool-tip are used to provide planar and distance constraints, and the optimization problem is solved using Levenberg-Marquardt algorithm. We demonstrate through experiments that SCALAR can reduce the mean and the maximum tool position error from 0.44 mm to 0.19 mm and from 1.41 mm to 0.50 mm, respectively.Note to Practitioners-Many industrial robotic applications require the robotic system to be calibrated in order to achieve the demanded accuracy. Unfortunately, existing calibration processes are often cumbersome or require expensive measurement systems. This paper presents a novel calibration method to calibrate simultaneously a 6-DoF industrial robot and a 2D laser scanner attached to the robot's flange. The method only uses the data from the robot and the laser, so there is no need for external measurement systems. The laser scanner can also be used after the calibration for subsequent robot tasks such as scanning a workpiece to accurately determine its location. The calibration setup only requires a flat plane and a sharp tool-tip attached on the robot's flange. The proposed method is easy to deploy and is more cost-effective than existing calibration methods.

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Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

Cited by 6 publications

References 29 publications

Orientation Modeling Using Quaternions and Rational Trigonometry

Orientation Modeling Using Quaternions and Rational Trigonometry

There's No Place Like Home: Visual Teach and Repeat for Emergency Return of Multirotor UAVs During GPS Failure

SCALAR: Simultaneous Calibration of 2D Laser and Robot Kinematic Parameters Using Planarity and Distance Constraints

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