Dynamic Target Tracking and Ingressing of a Small UAV Using Monocular Sensor Based on the Geometric Constraints

Abstract

In many applications of airborne visual techniques for unmanned aerial vehicles (UAVs), lightweight sensors and efficient visual positioning and tracking algorithms are essential in a GNSS-denied environment. Meanwhile, many tasks require the ability of recognition, localization, avoiding, or flying pass through these dynamic obstacles. In this paper, for a small UAV equipped with a lightweight monocular sensor, a single-frame parallel-features positioning method (SPPM) is proposed and verified for a real-time dynamic target tracking and ingressing problem. The solution is featured with systematic modeling of the geometric characteristics of moving targets, and the introduction of numeric iteration algorithms to estimate the geometric center of moving targets. The geometric constraint relationships of the target feature points are modeled as non-linear equations for scale estimation. Experiments show that the root mean square error percentage of static target tracking is less than 1.03% and the root mean square error of dynamic target tracking is less than 7.92 cm. Comprehensive indoor flight experiments are conducted to show the real-time convergence of the algorithm, the effectiveness of the solution in locating and tracking a moving target, and the excellent robustness to measurement noises.

FDA-SSD: Fast Depth-Assisted Single-Shot MultiBox Detector for 3D Tracking Based on Monocular Vision

“…The current works of depth estimation based on neural networks are still not mature enough, with a problem of a large amount of calculation and poor real-time performance, such as CNN-based monocular depth-estimation methods [20], multi-scale networks for DepthMap estimation methods [21], the method of using the Res-Net to solve the depth estimation [22], and the method of using the combination of CNN and a graphical model [23]. Considering the real-time problem, accurate positioning and stable tracking of 3D-space targets were achieved in this study based on the author's previous research work and geometrically constrained spatial-positioning method [10]. Our method considers the computational burden and real-time issues while ensuring the tracking accuracy.…”

“…This significantly reduces the computational burden of simultaneously calculating feature channels of all scales. Finally, a target-detection and tracking algorithm framework based on the FDA-SSD monocular sensor platform was established in the combination with the author's previous target-location method based on geometric constraints [10]. Specifically, an efficient and robust geometric constraint equation method was taken to solve the target space; the normalized depth information of the target in the current frame was fed back to the FDA-SSD of the next frame to select a detector at the matching scale for target detection.…”

“…Based on the FDA-SSD network and the author's previous work [10], a targetdetection-and-tracking algorithm framework for a monocular motion platform was established using the spatial-positioning method of geometric constraints. In this study, multiple sets of indoor target-tracking experiments were conducted with the indoor dynamic capture system as the ground truth.…”

MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

“…The target recognition and feature baseline extraction in Level 1 can refer to the previous work of the author [20]. The extraction method of the orb in Level 3 has been referred to by a large number of related studies.…”

“…For Level 1, the feature baseline is abstracted as the size feature of the target being tracked, such as the edge length, radius, etc., the extraction method is referred to our previous work [20]. The following focuses on feature baseline extraction methods in Levels 2 and 3.…”