A Novel and Simplified Extrinsic Calibration of 2D Laser Rangefinder and Depth Camera

It is too difficult to directly obtain the correspondence features between the two-dimensional (2D) laser-range-finder (LRF) scan point and camera depth point cloud, which leads to a cumbersome calibration process and low calibration accuracy. To address the problem, we propose a calibration method to construct point-line constraint relations between 2D LRF and depth camera observational features by using a specific calibration board. Through the observation of two different poses, we construct the hyperstatic equations group based on point-line constraints and solve the coordinate transformation parameters of 2D LRF and depth camera by the least square (LSQ) method. According to the calibration error and threshold, the number of observation and the observation pose are adjusted adaptively. After experimental verification and comparison with existing methods, the method proposed in this paper easily and efficiently solves the problem of the joint calibration of the 2D LRF and depth camera, and well meets the application requirements of multi-sensor fusion for mobile robots

End Pose Compensation System of Spraying Robots Based on Unscented Kalman Filter Algorithm

The spraying construction robot is unable to include information about ground leveling in the map when it is created, and when the robot operates according to the built map, the spraying clamping fixture at the working end of the spraying construction robot can not be parallel to the wall due to the lack of ground information. In order to compensate the posture error of the spraying fixture relative to the wall, a multi-sensor fusion method is proposed based on the unscented Kalman filter to compensate the posture of the spraying fixture.The state equation of the fixture posture is constructed from the data measured by the displacement measurement sensor， the equation of fixture posture measurement is constructed from the data measured by the gyroscope, and the optimal estimation of the fixture posture is obtained by using the unscented Kalman filter algorithm and transferring them to the robot, so as to achieve the purpose of compensating the posture error of the spraying fixture. Finally, the experimental platform is built to verify the feasibility of the error compensation system. The experimental results show that the positional error between the spraying fixture and the wall after error compensation is reduced to 0.005°

Training a Dataset Simulated Using RGB Images for an End-to-End Event-Based DoLP Recovery Network

Event cameras are bio-inspired neuromorphic sensors that have emerged in recent years, with advantages such as high temporal resolutions, high dynamic ranges, low latency, and low power consumption. Event cameras can be used to build event-based imaging polarimeters, overcoming the limited frame rates and low dynamic ranges of existing systems. Since events cannot provide absolute brightness intensity in different angles of polarization (AoPs), degree of linear polarization (DoLP) recovery in non-division-of-time (non-DoT) event-based imaging polarimeters is an ill-posed problem. Thus, we need a data-driven deep learning approach. Deep learning requires large amounts of data for training, and constructing a dataset for event-based non-DoT imaging polarimeters requires significant resources, scenarios, and time. We propose a method for generating datasets using simulated polarization distributions from existing red–green–blue images. Combined with event simulator V2E, the proposed method can easily construct large datasets for network training. We also propose an end-to-end event-based DoLP recovery network to solve the problem of DoLP recovery using event-based non-DoT imaging polarimeters. Finally, we construct a division-of-time event-based imaging polarimeter simulating an event-based four-channel non-DoT imaging polarimeter. Using real-world polarization events and DoLP ground truths, we demonstrate the effectiveness of the proposed simulation method and network

Thrust Improvement of a Biomimetic Robotic Fish by Using a Deformable Caudal Fin

In nature, live fish has various deformable fins which are capable to promote the swimming speed, efficiency, stability, and thrust generation. However, this feature is rarely possessed by current man-made biomimetic robotic fishes. In this paper, a novel deformable caudal fin platform is proposed to improve thrust generation of biomimetic robotic fish. First, the design of the deformable caudal fin is given, which includes a servo motor, a gear-based transmission mechanism, fin bones, and silica membrane. Second, an improved Central Pattern Generator (CPG) model was developed to coordinately control the flapping of the tail and the deformation of the caudal fin. More specifically, three deformation patterns, i.e., conventional nondeformable mode, sinusoidal-based mode, instant mode, of the caudal fin are investigated. Third, extensive experiments are conducted to explore the effects of deformation of the caudal fin on the thrust generation of the biomimetic robotic fish. It was found that the instant mode of the caudal fin has the largest thrust, which sees a 27.5% improvement compared to the conventional nondeformable mode, followed by the sinusoidal-based mode, which also sees an 18.2% improvement. This work provides a novel way to design and control the deformation of the caudal fin, which sheds light on the development of high-performance biomimetic robotic fish