Deep learning of monocular depth, optical flow and ego-motion with geometric guidance for UAV navigation in dynamic environments

Mumuni, Fuseini; Mumuni, Alhassan; Amuzuvi, Christian Kwaku

doi:10.1016/j.mlwa.2022.100416

Cited by 12 publications

(15 citation statements)

References 51 publications

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“…Some articles used specific software to run their tests, such as Google Earth (Michaelsen et al, 2011, 2012) and MAV3DSim (Lugo-Cárdenas et al, 2017. Regarding software, Song et al (2022) use the Unreal Engine as Konrad et al (2017); Lee and Kim (2022); Li et al (2018); LI and HU (2021); Mansouri et al (2020a); Mumuni et al (2022); Nakamura et al (2017); Tsiourva and Papachristos (2020); Wang et al (2017); Xu et al (2013); Zhang et al (2017); Zhao et al (2021a) the physics simulation engine, the same used by Fu et al (2022) within the AirSim simulation platform.…”

Section: Differences Over Methodsmentioning

confidence: 99%

“…Static environments are those where the only moving object is the flying vehicle. In this regard, only six papers (Bhusal et al, 2022;Cho et al, 2022;Chen and Lu, 2022;Lee and Kim, 2022;Mumuni et al, 2022;Narazaki et al, 2022) report that the environment was dynamic, and all the others environments were considered static.…”

Section: Benchmark Metrics Used For Comparisonmentioning

confidence: 99%

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UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

Ayala

Portela

Buarque

et al. 2023

Preprint

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Autonomous control for unmanned aerial vehicles (UAVs) has rapidly raised research interest in the last decade. Widely used in civilian, military, and private areas, UAVs have been implemented in surveillance, search and rescue, and delivery tasks, solving problems where a significant space must be covered and traveled. However, using UAVs for navigation problems with full autonomy in control, planning, localization, and mapping tasks remains an open challenge. This paper presents a systematic literature review on object-goal navigation using autonomous UAVs. Object-goal navigation inherits the same navigation challenges, such as control and navigation, in addition to estimating the target's location. The object-search problem requires to understand what object must be found and where it can be located.In this regard, survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. This review reveals essential issues related to autonomous navigation task dependencies and gaps in UAV development and framework standardization. Open challenges for autonomous UAV control under object-goal navigation must address the research on finding methods for problems, such as autonomy level and comparison metrics, considering safety, ethics, and legal implications.

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Section: Differences Over Methodsmentioning

confidence: 99%

Section: Benchmark Metrics Used For Comparisonmentioning

confidence: 99%

UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

Ayala

Portela

Buarque

et al. 2023

Preprint

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show abstract

“…In order to overcome the well-known challenges of dynamic environment, limitations of aerial datasets for training, and embedded hardware constraints, Mumuni et al [62] proposed a confidence-weighted adaptive network with geometric-guided refinement called Cowan-GGR. They designed and tested a lightweight real-time CNN for the UAV system, which included three deep models.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Deep Learning for Visual SLAM: The State-of-the-Art and Future Trends

Favorskaya

2023

Electronics

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Visual Simultaneous Localization and Mapping (VSLAM) has been a hot topic of research since the 1990s, first based on traditional computer vision and recognition techniques and later on deep learning models. Although the implementation of VSLAM methods is far from perfect and complete, recent research in deep learning has yielded promising results for applications such as autonomous driving and navigation, service robots, virtual and augmented reality, and pose estimation. The pipeline of traditional VSLAM methods based on classical image processing algorithms consists of six main steps, including initialization (data acquisition), feature extraction, feature matching, pose estimation, map construction, and loop closure. Since 2017, deep learning has changed this approach from individual steps to implementation as a whole. Currently, three ways are developing with varying degrees of integration of deep learning into traditional VSLAM systems: (1) adding auxiliary modules based on deep learning, (2) replacing the original modules of traditional VSLAM with deep learning modules, and (3) replacing the traditional VSLAM system with end-to-end deep neural networks. The first way is the most elaborate and includes multiple algorithms. The other two are in the early stages of development due to complex requirements and criteria. The available datasets with multi-modal data are also of interest. The discussed challenges, advantages, and disadvantages underlie future VSLAM trends, guiding subsequent directions of research.

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“…Simultaneous localization and mapping (SLAM) based solutions are ideal for unconstrained environments where changes in the positions of landmarks in the surroundings are common. These methods obtain input from sensors like IMU and LiDAR, however, they tend to increase the computational burden [15]. In the case of dynamic environments, the path plan must be continuously updated based on obstacles that are being detected during flight and appear in the drone's path.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Navigation in Unconstrained Environments Using Reinforcement Learning Algorithms

Chronis,

Anagnostopoulos,

Politi

et al. 2023

IEEE Access

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The potential for the use of drones in logistics and transportation is continuously growing, with multiple applications both in urban and rural environments. The safe navigation of drones in such environments is a major challenge that requires sophisticated algorithms and systems, that can quickly and efficiently estimate the situation, find the shortest path to the target, and detect and avoid obstacles. Traditional path planning algorithms are unable to handle the dynamic and uncertain nature of real environments, while traditional machine learning models are insufficient due to the constantly changing conditions that affect the drone location and the location of obstacles. Reinforcement learning (RL) algorithms have been widely used for autonomous navigation problems, however, computational complexity and energy demands of such methods can become a bottleneck to the execution of UAV flights. In this paper, we propose the use of a minimum set of sensors and reinforcement learning (RL) algorithms for the safe and efficient navigation of drones in urban and rural environments. Our approach considers the complex and dynamic nature of such environments by incorporating real-time data from low-cost onboard sensors. After performing a thorough review of the existing solutions in drone path planning and navigation in 3-D environments, we experimentally evaluate our proposed approach in a simulated environment, and in various scenarios. The test results demonstrate the effectiveness of the proposed RL-based approach in the navigation of drones in complex and unconstrained environments. The implemented approach can serve as a basis for the development of advanced and robust navigation systems for drones, which can improve safety and efficiency in transportation applications in the near future.INDEX TERMS actor-critic algorithm, drone navigation, dynamic path planning, proximal policy optimization, reinforcement learning.

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Deep learning of monocular depth, optical flow and ego-motion with geometric guidance for UAV navigation in dynamic environments

Cited by 12 publications

References 51 publications

UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

Deep Learning for Visual SLAM: The State-of-the-Art and Future Trends

Dynamic Navigation in Unconstrained Environments Using Reinforcement Learning Algorithms

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