A ROS Multi-Tier UAV Localization Module Based on GNSS, Inertial and Visual-Depth Data

Antonopoulos, Angelos; Lagoudakis, Michail G.; Partsinevelos, Panagiotis

doi:10.3390/drones6060135

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…Utilizing the ROS platform 35 , we conducted UAV autonomous flight experiments across various simulated environments and compared them with other algorithms to assess the proposed algorithm's effectiveness. During the experiment, the UAV faced limitations in acquiring global obstacle information.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs

Sun,

Ding

et al. 2024

Sci Rep

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Unmanned aerial vehicles (UAVs) have become the focus of current research because of their practicability in various scenarios. However, current local path planning methods often result in trajectories with numerous sharp or inflection points, which are not ideal for smooth UAV flight. This paper introduces a UAV path planning approach based on distance gradients. The key improvements include generating collision-free paths using collision information from initial trajectories and obstacles. Then, collision-free paths are subsequently optimized using distance gradient information. Additionally, a trajectory time adjustment method is proposed to ensure the feasibility and safety of the trajectory while prioritizing smoothness. The Limited-memory BFGS algorithm is employed to efficiently solve optimal local paths, with the ability to quickly restart the trajectory optimization program. The effectiveness of the proposed method is validated in the Robot Operating System simulation environment, demonstrating its ability to meet trajectory planning requirements for UAVs in complex unknown environments with high dynamics. Moreover, it surpasses traditional UAV trajectory planning methods in terms of solution speed, trajectory length, and data volume.

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Section: Experiments Results and Analysismentioning

confidence: 99%

Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs

Sun,

Ding

et al. 2024

Sci Rep

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show abstract

“…However, in the case of partial information loss in a GNSS, the localization system must rely mainly on LiDAR data to determine the position and orientation of the UAV. Using advanced data fusion algorithms, such as extended Kalman filters or particle filters, information from different sensors (such as LiDAR and a GNSS) can be efficiently integrated to obtain accurate estimates of the position and orientation of the UAV despite the partial loss of information [26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Pacini,

Dini,

Fanucci

2024

Electronics

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Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness.

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“…Most of them mainly begin with two aspects of an integrated system. One aspect is to improve the system input, such as preprocessing the data, or adding auxiliary sensors to form a multi-source information fusion system and using other auxiliary sensor information to correct the INS [15,16]. The other aspect is to improve the processing methods of INS and GNSS data.…”

Section: Introductionmentioning

confidence: 99%

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

et al. 2023

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When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

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A ROS Multi-Tier UAV Localization Module Based on GNSS, Inertial and Visual-Depth Data

Cited by 13 publications

References 36 publications

Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs

Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

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