Monocular Trail Detection and Tracking Aided by Visual SLAM for Small Unmanned Aerial Vehicles

Silva, André; Mendonca, Ricardo; Santana, Pedro

doi:10.1007/s10846-019-01033-x

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…In addition, improving depth calculation in complex environments to meet the requirements of actual applications is still a hard task (Ming et al, 2021). In general, depth-based approaches have a significant volume of calculations due to the preparation of three-dimensional (3D) information from the surrounding environment (Silva et al, 2020). Moreover, due to the limitation on heavy processing and the requirement for a powerful GPU, this problem is more severe in MAVs (Pestana et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Developing an expansion‐based obstacle detection using panoptic segmentation

Pirasteh,

Varshosaz,

Badrloo

et al. 2024

Journal of Field Robotics

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Safe Micro Aerial Vehicle (MAV) navigation requires detecting and avoiding obstacles. For safe MAV navigation, expansion‐based algorithms are effective for detecting obstacles. However, accurate and real‐time obstacle detection is a fundamental challenge. Some traditional methods focus on extracting geometric features from images and applying geometric constraints to identify potential obstacles. Others may leverage machine learning algorithms for object detection and classification, using features such as texture, shape, and context to distinguish obstacles from background clutter. The choice of approach depends on factors such as the specific requirements of the application, the complexity of the scene, and the available computational resources. Since obstacles, in reality, take the form of objects (e.g., persons, walls, pillars, trees, automobiles, and other structures), it is preferable to represent them according to human comprehension and as objects. Therefore, the objective of this study is to reflect on the previous research and address the issues mentioned above by extracting objects from the fisheye image using a panoptic deep‐learning network. The extracted object regions are, then, used to identify obstacles with a novel area‐based expansion rate we developed in a previous study. We compared the accuracy of obstacle detection in our proposed method to the existing method when moving forward and to the right; thus, we improved it between 10% and 18%, respectively. In addition, compared with the existing method, and due to replacing a single object with multiple regions, obstacle‐detection runtime for forward and right direction is 15.71 and 25.5 times faster, respectively, and the required match points have decreased by 49% and 55%.

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Section: Related Workmentioning

confidence: 99%

Developing an expansion‐based obstacle detection using panoptic segmentation

Pirasteh,

Varshosaz,

Badrloo

et al. 2024

Journal of Field Robotics

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“…Yun-Won et al use Monocular RGB vision sensor to build a double-layer reinforcement learning network structure, which can achieve a good obstacle avoidance effect and lay a good foundation for the realization of path planning [ 9 ]. Silva et al obtained the optimal parameters by optimizing the strategy parameters [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

Research on SLAM Road Sign Observation Based on Particle Filter

Wang

2022

Computational Intelligence and Neuroscience

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With the development of computer hardware technology, the real-time problem of visual target tracking algorithm increasingly depends on hardware solutions. The core problem of visual target tracking is how to enhance the robustness of tracking algorithm to various complex background environments and various interference factors. Aiming at overcoming the defect that the traditional SLAM (simultaneous localization and map building) algorithm based on EKF (extended Kalman filter) has a slow repair speed for environmental interference, a Monocular SLAM_WOCPF (Monocular vision SLAM based on weight optimization combined particle filter) algorithm is proposed. The weights of all particles are reoptimized in the particle set and they are combined with the tendency of particles to degenerate and deplete. In this way, the chance of self replication of low weight particles is increased, thus increasing the diversity of the whole sample. Furthermore, the improved PF (particle filter) algorithm is applied to solve the problem of road sign observation of mobile robots, so as to expand its application scope. The results show that the mean road sign errors of the Monocular SLAM_WOCPF algorithm in two noise environments are 0.332/m and 0.441/m. The conclusion shows that the Monocular SLAM_WOCPF road sign observation method proposed in this paper can effectively improve the matching success rate of visual road signs and improve the observation quality.

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“…This enables the driver to control the vehicle without approaching it. More convenient services become available, like remote door lock, tracking the trail of vehicles [18] , finding the vehicle in a parking lot [19] , and tracing a stolen vehicle. For example, a logistic company can track the fleet in real time, such that it can provide a more accurate estimation of the good delivery time.…”

Section: Remote Vehicle Servicementioning

confidence: 99%

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Luo

et al. 2020

Intell. and Converged Netw.

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Internet of Vehicles (IoV) is a distributed network of connected cars, roadside infrastructure, wireless communication networks, and central cloud platforms. Wireless recommendations play an important role in the IoV network, for example, recommending appropriate routes, recommending driving strategies, and recommending content. In this paper, we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.

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Monocular Trail Detection and Tracking Aided by Visual SLAM for Small Unmanned Aerial Vehicles

Cited by 12 publications

References 30 publications

Developing an expansion‐based obstacle detection using panoptic segmentation

Developing an expansion‐based obstacle detection using panoptic segmentation

Research on SLAM Road Sign Observation Based on Particle Filter

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

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