Infrared-Inertial Navigation for Commercial Aircraft Precision Landing in Low Visibility and GPS-Denied Environments

Zhang, Lei; Zhai, Zhengjun; He, Lang; Wen, Pengcheng; Niu, Wensheng

doi:10.3390/s19020408

Cited by 19 publications

(16 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of an active sensor on those scenarios can tackle some of those limitations and provide alternatives for autonomous navigation, as they have in other domains of study [31]. Infrared-inertial precision landing [32] and thermal-inertial localization [33,34] are examples of active sensor solutions utilizing infrared wavelengths. Additionally, advancements concerning navigation, mapping and geolocalization have been made with Light Detection and Ranging (LiDAR) [35] and Synthetic Aperture Radar (SAR) sensors [36,37].…”

Section: Introductionmentioning

confidence: 99%

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Salles¹,

Velho

Shiguemori³

2022

Remote Sensing

View full text Add to dashboard Cite

In this paper we post-process and evaluate the position estimation of pairs of template windows and geo-referenced images generated from LiDAR cloud point data using the Normalized Cross-Correlation (NCC) method. We created intensity, surface and terrain pairs of images for use with template matching, with 5 m pixel spacing, through binning. We evaluated square and circular binning approaches, without filtering the original data. Template matching achieved approximately 7 m root mean square error (RMSE) on intensity and surface templates on the respective geo-referenced images, while on terrain templates it had many mismatches due to insufficient terrain features over the assumed flight transect. Analysis of NCC showed the possibility of rejecting bad matches of intensity and surface templates, but terrain templates required an additional criteria of flatness for rejection. The combined NCC of intensity, surface and terrain proved stable for rejection of bad matches and had the lowest RMSE. Filtering outliers from surface images changed very little the accuracy of the matches, but greatly improved correlation values, indicating that the forest canopy might have the best features for geo-localization with template matching. Position estimation is essential for autonomous navigation of aerial vehicles and the these experiments with LiDAR data show potential for localization over densely forested regions where methods using optical camera data may fail to acquire distinguishable features.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Salles¹,

Velho

Shiguemori³

2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Vision-based navigation in general has three main components: sensor type (ultraviolet [ 10 ], infrared [ 11 , 12 ], visible RGB/mono [ 11 , 13 ], stereo [ 14 ]), a priori data (terrain model [ 10 ], satellite images [ 15 ], UAV images [ 16 ], 3D position of key features [ 11 , 13 , 14 ]), and data accumulation (single frame [ 11 , 13 , 14 ] and optic flow/SLAM [ 6 , 16 , 17 , 18 , 19 , 20 ]). Runway relative navigation for autonomous landing is a special case.…”

Section: Introductionmentioning

confidence: 99%

“…Binary images of a complete polygon model of the runway with taxiway exits is generated in [ 21 ] from a neighborhood of the IMU/GPS pose, and these images are compared with the shifted hue channel of the HSV version of an RGB camera image to get the best fit. From IMU pose and runway geoinformation, four edges are rendered in [ 12 ] and line features are fitted in the real image which defined the homography from the synthetic image to the real image. Four corner points of the runway (parallel lines with four points) are also enough for 6D pose calculations.…”

Section: Introductionmentioning

confidence: 99%

Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach

Hiba

Gáti

Manecy

2021

Sensors

View full text Add to dashboard Cite

Precise navigation is often performed by sensor fusion of different sensors. Among these sensors, optical sensors use image features to obtain the position and attitude of the camera. Runway relative navigation during final approach is a special case where robust and continuous detection of the runway is required. This paper presents a robust threshold marker detection method for monocular cameras and introduces an on-board real-time implementation with flight test results. Results with narrow and wide field-of-view optics are compared. The image processing approach is also evaluated on image data captured by a different on-board system. The pure optical approach of this paper increases sensor redundancy because it does not require input from an inertial sensor as most of the robust runway detectors.

show abstract

“…The landing trajectory is calculated and sent to the UAV via radio to perform Command and Control (C2). A vision system is more reliable in environments where we can have a Global Positioning System (GPS) or radar jamming (Acuna, Zhang, & Willert, 2018; L. Zhang, Zhai, He, Wen, & Niu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Santos

Lobo

Bernardino

2020

Journal of Field Robotics

View full text Add to dashboard Cite

Particle Filters (PFs) have been successfully used in three-dimensional (3D) modelbased pose estimation. Typically, these filters depend on the computation of importance weights that use similarity metrics as a proxy to approximate the likelihood function. In this paper, we explore the use of a two-stage 3D model-based approach based on a PF for single-frame pose estimation. First, we use a classifier trained in a synthetic data set for Unmanned Aerial Vehicle (UAV) detection and a pretrained database indexed by bounding boxes properties to obtain an initial rough pose estimate. Second, we employ optimization algorithms to optimize the used similarity metrics and decrease the obtained error. We have tested four different algorithms: (a) Particle Filter Optimization (PFO), (b) Particle Swarm Optimization (PSO), (c) modified PSO, and (d) an approach based on the evolution strategies present in the genetic algorithms named Genetic Algorithm-based Framework (GAbF). To check the quality of the estimate on each iteration, we have tested several similarity metrics (color, edge, and mask-based) based on the UAV Computer-Aided Design (CAD) model. The framework is applied to the outdoor pose estimation of a fixed-wing UAV for autonomous landing in a Fast Patrol Boat (FPB). We extend our previous approach by adopting a better problem formulation, using Deep Neural Networks (DNNs) for UAV detection, making the comparison between the used similarity metrics, comparing pose optimization schemes, and showing new results.The future work will focus on the inclusion of this scheme in a tracking architecture to increase the accuracy of the result between observations.

show abstract

Infrared-Inertial Navigation for Commercial Aircraft Precision Landing in Low Visibility and GPS-Denied Environments

Cited by 19 publications

References 46 publications

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region

Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Contact Info

Product

Resources

About