LSTM and Filter Based Comparison Analysis for Indoor Global Localization in UAVs

Abstract

Deep learning (DL) based localization and Simultaneous Localization and Mapping (SLAM) has recently gained considerable attention demonstrating remarkable results. Instead of constructing handcrafted algorithms through geometric theories, DL based solutions provide a data-driven solution to the problem. Taking advantage of large amounts of training data and computing capacity, these approaches are increasingly developing into a new field that offers accurate and robust localization systems. In this work, the problem of global localization for unmanned aerial vehicles (UAVs) is analyzed by proposing a sequential, end-to-end, and multimodal deep neural network based monocular visual-inertial localization framework. More specifically, the proposed neural network architecture is threefold ; a visual feature extractor convNet network, a small IMU integrator bi-directional long short-term memory (LSTM), and a global pose regressor bi-directional LSTM network for pose estimation. In addition, by fusing the traditional IMU filtering methods instead of LSTM with the convNet, a more time-efficient deep pose estimation framework is presented. It is worth pointing out that the focus in this study is to evaluate the precision and efficiency of visual-inertial (VI) based localization approaches concerning indoor scenarios. The proposed deep global localization is compared with the various state-of-the-art algorithms on indoor UAV datasets, simulation environments and real-world drone experiments in terms of accuracy and time-efficiency. In addition, the comparison of IMU-LSTM and IMU-Filter based pose estimators is also provided by a detailed analysis. Experimental results show that the proposed filter-based approach combined with a DL approach has promising performance in terms of accuracy and time efficiency in indoor localization of UAVs.

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

“…Although it is difficult for the UAV to perform tasks in indoor environments, studies on SLAM applications for UAV localization in indoor environments have recently increased. In a SLAM study that we previously conducted [100], the results of the previous indoor state-of-the-art visual-inertial SLAM (VISLAM)/visualinertial odometry (VIO) studies and the method that we suggested were compared. With the deep learning-based approach, more successful localization (odometry) was achieved with UAV indoors.…”

“…The development of such applications will also make a significant contribution to smart greenhouses in the future. [100].…”

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

“…Camera-based localization approaches have gained much popularity because of providing valuable information about any object/environment, explored by the robotic devices such as aerial vehicles and drones, with low-cost implementations and easy hardware setup [36]. SLAM mechanism is one of those localization approaches which performs both localization and mapping, as the name suggests [37] and used in applications such as virtual and augmented reality [38].…”

“…DL-based algorithms have proven to be effective in performing identification of the object/environment, classification of the image data and semantic segmentation [36], [39]. These algorithms help in addressing problems such as the instability of the system operations generated from the pose estimation of the camera (due to this being affected by the vibration of the mobile bodies, brightness of the scene etc.)…”

An Unmanned Aerial Vehicle Indoor Low-Computation Navigation Method Based on Vision and Deep Learning

“…Numerous innovative solutions have been proposed, encompassing VLC-based indoor positioning, multisensory fusion leveraging extended Kalman filters, optical flow-centric systems, and the data amalgamation of the ultrawideband (UWB) and IMU [ 13 , 14 , 15 , 16 ]. Notably, deep learning has recently emerged as a favored solution [ 17 ].…”