Junxiang Lian

Design of a Device for Sky Light Polarization Measurements

Sky polarization patterns can be used both as indicators of atmospheric turbidity and as a sun compass for navigation. The objective of this study is to improve the precision of sky light polarization measurements by optimal design of the device used. The central part of the system is composed of a Charge Coupled Device (CCD) camera; a fish-eye lens and a linear polarizer. Algorithms for estimating parameters of the polarized light based on three images are derived and the optimal alignments of the polarizer are analyzed. The least-squares estimation is introduced for sky light polarization pattern measurement. The polarization patterns of sky light are obtained using the designed system and they follow almost the same patterns of the single-scattering Rayleigh model. Deviations of polarization angles between observation and the theory are analyzed. The largest deviations occur near the sun and anti-sun directions. Ninety percent of the deviations are less than 5° and 40% percent of them are less than 1°. The deviations decrease evidently as the degree of polarization increases. It also shows that the polarization pattern of the cloudy sky is almost identical as in the blue sky.

A Novel Angle Computation and Calibration Algorithm of Bio-Inspired Sky-Light Polarization Navigation Sensor

Navigation plays a vital role in our daily life. As traditional and commonly used navigation technologies, Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) can provide accurate location information, but suffer from the accumulative error of inertial sensors and cannot be used in a satellite denied environment. The remarkable navigation ability of animals shows that the pattern of the polarization sky can be used for navigation. A bio-inspired POLarization Navigation Sensor (POLNS) is constructed to detect the polarization of skylight. Contrary to the previous approach, we utilize all the outputs of POLNS to compute input polarization angle, based on Least Squares, which provides optimal angle estimation. In addition, a new sensor calibration algorithm is presented, in which the installation angle errors and sensor biases are taken into consideration. Derivation and implementation of our calibration algorithm are discussed in detail. To evaluate the performance of our algorithms, simulation and real data test are done to compare our algorithms with several exiting algorithms. Comparison results indicate that our algorithms are superior to the others and are more feasible and effective in practice.

An evaluation of skylight polarization patterns for navigation.

Skylight polarization provides a significant navigation cue for certain polarization-sensitive animals. However, the precision of the angle of polarization (AOP) of skylight for vehicle orientation is not clear. An evaluation of AOP must be performed before it is utilized. This paper reports an evaluation of AOP of skylight by measuring the skylight polarization patterns of clear and cloudy skies using a full-sky imaging polarimetry system. AOP measurements of skylight are compared with the pattern calculated by the single-scattering Rayleigh model and these differences are quantified. The relationship between the degree of polarization (DOP) and the deviation of AOP of skylight is thoroughly studied. Based on these, a solar meridian extracted method is presented. The results of experiments reveal that the DOP is a key parameter to indicate the accuracy of AOP measurements, and all the output solar meridian orientations extracted by our method in both clear and cloudy skies can achieve a high accuracy for vehicle orientation.

Design and Calibration of a Novel Camera-Based Bio-Inspired Polarization Navigation Sensor

The remarkable ability of animals using the skylight polarization pattern provides a significant inspiration for the autonomous robotic navigation. The bio-inspired polarization navigation approach with advantages of efficiency and reliability has aroused much interesting for experts to perform further research. The sensor of measuring the skylight polarization pattern plays a key role in bio-inspired polarization navigation. A novel camera-based bionic polarization navigation sensor is designed and implemented. We propose a robust measurement method based on the least squares by utilizing all outputs of measurement units. For improving the precision of measurement, the errors of sensor are analyzed, and an effective calibration algorithm is proposed in detail. The experiment results show that the measurement accuracy is improved greatly after calibration. Finally, to evaluate the performance of our sensor, experiments of measuring the actual skylight polarization pattern are performed and compared with the results from the single-scattering Rayleigh theory. The comparison results indicate that our sensor can achieve an effective and accuracy measurement in practice.

Bionic Orientation and Visual Enhancement With a Novel Polarization Camera

The remarkable polarization vision of animals provides a significant inspiration for robotic navigation and visual enhancement, as polarization pattern provides additional information besides spectral signatures. A novel bio-inspired polarization camera is proposed in this paper, which can realize real-time image-based polarization measurement. The composition of the system is described and the optimal estimation of the polarization state is derived based on the least square algorithm. This paper concentrates particularly on the camera orientation algorithms and visual enhancement methods with it. To estimate the camera’s heading angle with the skylight polarization pattern, the sun vector is established as an optimization problem of finding the minimum eigenvector. The solar meridian is also estimated from the degree of polarization pattern by detecting reflectional symmetry axes. The result shows that the measured polarization patterns are very close to the theory. The maximum orientation error of the proposed method based on angle of polarization is about 0.5°. The average error is 0.012° with standard deviation of 0.28°. Thus, the novel polarization camera could be used as sun compass. When observing scenes in distance, the polarization camera is used to decouple the airlight from the object radiance, which results in much better contrast. More importantly, the polarization information is helpful for scene identification and object detection. The result also shows that the polarization camera can reasonably cope with the semi-reflection problem.

Compass information extracted from a polarization sensor using a least-squares algorithm

Skylight polarization provides a significant navigation cue for certain polarization-sensitive animals. We designed a polarization navigation sensor based on the polarization sensitivity mechanism of insects. In this paper, the principle of our polarization navigation sensor is introduced. The relationship between the degree of polarization (DOP) and the error of the angle of polarization (AOP) is examined. A new DOP and AOP calculation algorithm using a linear least-squares algorithm is presented. The results of simulation and experiments reveal the essentiality of DOP calculation and demonstrate the efficiency and accuracy of the proposed algorithm.

Design and Calibration of a Novel Bio-Inspired Pixelated Polarized Light Compass

Animals, such as Savannah sparrows and North American monarch butterflies, are able to obtain compass information from skylight polarization patterns to help them navigate effectively and robustly. Inspired by excellent navigation ability of animals, this paper proposes a novel image-based polarized light compass, which has the advantages of having a small size and being light weight. Firstly, the polarized light compass, which is composed of a Charge Coupled Device (CCD) camera, a pixelated polarizer array and a wide-angle lens, is introduced. Secondly, the measurement method of a skylight polarization pattern and the orientation method based on a single scattering Rayleigh model are presented. Thirdly, the error model of the sensor, mainly including the response error of CCD pixels and the installation error of the pixelated polarizer, is established. A calibration method based on iterative least squares estimation is proposed. In the outdoor environment, the skylight polarization pattern can be measured in real time by our sensor. The orientation accuracy of the sensor increases with the decrease of the solar elevation angle, and the standard deviation of orientation error is 0.15∘ at sunset. Results of outdoor experiments show that the proposed polarization navigation sensor can be used for outdoor autonomous navigation.

Improved Seq SLAM for Real-Time Place Recognition and Navigation Error Correction

Place recognition plays an important role in long term navigation in challenging environment and Seq SLAM has achieved quite remarkable results. In this paper, we mainly adopt three strategies to improve the original Seq SLAM algorithm: integrating Seq SLAM with odometry, optimizing sequence searching strategy and multi-scale sequence matching. The improved algorithm is evaluated using the KITTI dataset. The template library is created online using navigation information from the sliding-window visual-inertial odometer. When a place is recognized, the corresponding information is used as observation of the filter. The result shows the superiority of the proposed method in real-time place recognition. The optimized sequence searching strategy performs much better in minor deviations. Meanwhile, the advantages of longer sequence match (higher recall rate) and short sequence match (precise location) are combined together. At last, the navigation errors are greatly reduced by close-loop detection. The overall position error of odometer with Seq SLAM is 20.3m (0.55% of the trajectory), which is much smaller than the navigation errors of the single odometer (32.0m, 0.86%).

Polarized Light Compass-Aided Visual-Inertial Navigation Under Foliage Environment

This paper presents a method to extract heading information from the sky light polarization pattern and successfully implements the polarized light compass (PL-compass) under foliage environment with heavy occlusions for the first time. A classifier based on the primal support vector machine is trained to detect the sky area, and the random sample consensus algorithm is used to choose the inliers among the sky area. The sun vector is built upon the valid measurements. The attitude angles (Roll and Pitch) from the inertial navigation system (INS) are used to establish the Zenith direction when the platform tilts during traveling. The results show that the PL-compass can provide accurate heading angle (RMSE = 1.58°) by seeing only a tiny part of the skylight (minimum of 0.028%) in the dynamic vehicle test. The PL-compass is also integrated with the visual INS based on the indirect extended Kalman filter to improve the navigation accuracy. Each time a new measurement becomes available, the states estimated by the INS are used to check the consistency of the innovation and reject the outliers. The results show that the position errors of the integrated navigation system are greatly reduced with the help of the PL-compass. The estimated trajectory tracks the ground truth very well. The position error is 25.9 m (1.03% of the distance traveled) with heading errors less than 1° in the experiment.

A Novel Calibration Model of Polarization Navigation Sensor

In this paper, a polarization navigation sensor is designed based on the polarization sensitivity mechanisms of insects. A new calibration model by taking full advantage of both reference angle of polarization and constant degree of polarization is presented to determine calibration parameters. Unlike existing calibration algorithms, the proposed algorithm makes the calibration problem well-posed. The results of simulations and experiments show that the proposed algorithm is more stable than the compared methods for the calibration applications of polarization navigation sensors.