An adaptive anti-disturbance navigation method for polarized skylight-based autonomous integrated navigation system

Dou, Qingfeng; Du, Tao; Qiu, Zhenbing; Wang, Shanpeng; Jian, Yang

doi:10.1016/j.measurement.2022.111847

Cited by 16 publications

(3 citation statements)

References 34 publications

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“…As far as we know, only PbCs have been combined with Integrated Navigation Systems (INS). They could be either based on a PbC/INS with a heading accuracy ranging from 0.08° to 0.8° in sunny weather, but 0.8° to 1.5° in cloudy conditions [ 179 , 240 , 241 , 243 , 244 , 245 , 246 ], or PbC/GNSS/INS with a heading accuracy ranging from 0.02° to 6.5° [ 175 , 247 , 248 , 249 ], or PbC/SLAM/INS with a heading accuracy from 0.28° to 4.7° and a geographical position error from 1.96 m to 8.7 m [ 214 , 250 , 251 , 252 , 253 ]. The heading and trajectory accuracy can therefore be improved by about 40% compared to conventional navigation systems in complex outdoor environments [ 22 ].…”

Section: Polarized Vision For Robotics Navigationmentioning

confidence: 99%

Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

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Section: Polarized Vision For Robotics Navigationmentioning

confidence: 99%

Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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“…In the last few years, some new orientation methods have been developed which use polarized skylight for different navigation applications [34][35][36][37][38][39][40]. However, considering their instruments/equipments and how they use sky polarization, these sky-polarization-based modern navigation methods differ considerably from the SPVN.…”

Section: Plos Onementioning

confidence: 99%

Speedy bearings to slacked steering: Mapping the navigation patterns and motions of Viking voyages

Takacs,

Szaz,

Pereszlenyi

et al. 2023

PLoS ONE

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Viking sailors ruled the North Atlantic Ocean for about three hundred years. Their main sailing route was the 60° 21’ 55’’ latitude between Norway and Greenland. Although they did not have a magnetic compass, in sunshine they used a sun-compass to determine the geographical north (solar Viking navigation: SVN). It has been hypothesized that when the Sun was invisible, Viking navigators determined the direction of polarization of skylight with sunstones (dichroic/birefringent crystals), and then estimated the geographical north using the sun-compass (sky-polarimetric Viking navigation: SPVN). Many details of the hypothetical SPVN have been thoroughly revealed in psychophysical laboratory and planetarium experiments. Combining these results with measured celestial polarization patterns, the success of SPVN was obtained as functions of sailing, meteorological and navigation parameters (sunstone type, sailing date, navigation periodicity, night sailing, cloudiness conditions). What was so far lacking in this experimental and computational archeological approach is the study of the success of SVN and a combined navigation using solar cues in sunshine (SVN) and sky polarization at invisible Sun (SPVN), the latter being the most realistic method. In this work we determine the success of the sole SVN and the combined SVN-SPVN relative to the mere SPVN for three navigator types (determining the intended sailing direction with large, medium or small frequencies) at spring equinox and summer solstice, with and without night sailing. We found that to maximize the sailing success, navigators had to choose different navigation methods depending on the navigation frequency. Using sky polarization with very frequent navigation, resulted in the highest chance to survive a three-week voyage from Norway to Greenland.

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“…GNSS must rely on satellite navigation instructions and cannot independently complete navigation calculations. It is highly vulnerable to the enemy's priority attacks during wartime, resulting in satellite denial [4]. At the same time, satellites are susceptible to interference and deception, which significantly limits their application scenarios [5,6].…”

Section: Introductionmentioning

confidence: 99%

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

Jiang,

Luo,

Liu

2023

Remote Sensing

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Scene-matching navigation is one of the essential technologies for achieving precise navigation in satellite-denied environments. Selecting suitable-matching areas is crucial for planning trajectory and reducing yaw. Most traditional selection methods of suitable-matching areas use hierarchical screening based on multiple feature indicators. However, these methods rarely consider the interrelationship between different feature indicators and use the same set of screening thresholds for different categories of images, which has poor versatility and can easily cause mis-selection and omission. To solve this problem, a suitable-matching areas’ selection method based on multi-level saliency is proposed. The matching performance score is obtained by fusing several segmentation levels’ salient feature extraction results and performing weighted calculations with the sub-image edge density. Compared with the hierarchical screening methods, the matching performance of the candidate areas selected by our algorithm is at least 22.2% higher, and it also has a better matching ability in different scene categories. In addition, the number of missed and wrong selections is significantly reduced. The average matching accuracy of the top three areas selected by our method reached 0.8549, 0.7993, and 0.7803, respectively, under the verification of multiple matching algorithms. Experimental results show this paper’s suitable-matching areas’ selection method is more robust.

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An adaptive anti-disturbance navigation method for polarized skylight-based autonomous integrated navigation system

Cited by 16 publications

References 34 publications

Passive Polarized Vision for Autonomous Vehicles: A Review

Passive Polarized Vision for Autonomous Vehicles: A Review

Speedy bearings to slacked steering: Mapping the navigation patterns and motions of Viking voyages

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

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