This article presents a comprehensive review of and analysis of state-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on canopy-level and high-speed scenarios. This article presents a comprehensive exploration of sensor technologies suitable for UAV mapping, assessing their capabilities to provide measurements that meet the requirements of fast UAV mapping. Furthermore, the study conducts extensive experiments in a simulated environment to evaluate the performance of three distinct mapping algorithms: Direct Sparse Odometry (DSO), Stereo DSO (SDSO), and DSO Lite (DSOL). The experiments delve into mapping accuracy and mapping speed, providing valuable insights into the strengths and limitations of each algorithm. The results highlight the versatility and shortcomings of these algorithms in meeting the demands of modern UAV applications. The findings contribute to a nuanced understanding of UAV mapping dynamics, emphasizing their applicability in complex environments and high-speed scenarios. This research not only serves as a benchmark for mapping algorithm comparisons but also offers practical guidance for selecting sensors tailored to specific UAV mapping applications.

Tunnel construction using the drill-and-blast method requires the 3D measurement of the excavation front to evaluate underbreak locations. Considering the inspection and measurement task's safety, cost, and efficiency, deploying lightweight autonomous robots, such as unmanned aerial vehicles (UAV), becomes more necessary and popular. Most of the previous works use a prior map for inspection viewpoint determination and do not consider dynamic obstacles. To maximally increase the level of autonomy, this paper proposes a vision-based UAV inspection framework for dynamic tunnel environments without using a prior map. Our approach utilizes a hierarchical planning scheme, decomposing the inspection problem into different levels. The high-level decision maker first determines the task for the robot and generates the target point. Then, the mid-level path planner finds the waypoint path and optimizes the collision-free static trajectory. Finally, the static trajectory will be fed into the low-level local planner to avoid dynamic obstacles and navigate to the target point. Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM) pipeline is applied to generate the 3D shape of the target. To our best knowledge, this is the first time autonomous inspection has been realized in unknown and dynamic tunnel environments. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface. Our software is available on GitHub as an open-source ROS package.

Iran announced Thursday that it has identified the alleged mastermind behind dual suicide bombing attacks that left nearly 100 people dead at a recent memorial for late Gen. Qassem Soleimani, who was killed years ago by a U.S. drone strike. The IRNA news agency carried a statement by the intelligence ministry saying the main suspect who planned the Jan. 3 attack in Kerman, a city southeast of the Iranian capital of Tehran, was a Tajik national known by his alias Abdollah Tajiki. Tajiki reportedly entered the country in mid-December by crossing Iran's southeast border, and left two days before the attack, after making the bombs. One bomber first detonated his explosives at the ceremony in Kerman, then another attacked 20 minutes later as emergency workers and other people tried to help the wounded from the first explosion, according to The Associated Press. The report identified one of the bombers by his family name of Bozrov, saying the man was 24 years old and had Tajik and Israeli nationality.

Real-time and accurate information on fine-grained changes in crop cultivation is of great significance for crop growth monitoring, yield prediction and agricultural structure adjustment. Aiming at the problems of serious spectral confusion in visible high-resolution unmanned aerial vehicle (UAV) images of different phases, interference of large complex background and salt-and-pepper noise by existing semantic change detection (SCD) algorithms, in order to effectively extract deep image features of crops and meet the demand of agricultural practical engineering applications, this paper designs and proposes an agricultural geographic scene and parcel-scale constrained SCD framework for crops (AGSPNet). AGSPNet framework contains three parts: agricultural geographic scene (AGS) division module, parcel edge extraction module and crop SCD module. Meanwhile, we produce and introduce an UAV image SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple semantic variation types of crops in complex geographical scene. We conduct comparative experiments and accuracy evaluations in two test areas of this dataset, and the results show that the crop SCD results of AGSPNet consistently outperform other deep learning SCD models in terms of quantity and quality, with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over the sub-optimal method. The method proposed in this paper can clearly detect the fine-grained change information of crop types in complex scenes, which can provide scientific and technical support for smart agriculture monitoring and management, food policy formulation and food security assurance.

In challenging terrains, constructing structures such as antennas and cable-car masts often requires the use of helicopters to transport loads via ropes. The swinging of the load, exacerbated by wind, impairs positioning accuracy, therefore necessitating precise manual placement by ground crews. This increases costs and risk of injuries. Challenging this paradigm, we present Geranos: a specialized multirotor Unmanned Aerial Vehicle (UAV) designed to enhance aerial transportation and assembly. Geranos demonstrates exceptional prowess in accurately positioning vertical poles, achieving this through an innovative integration of load transport and precision. Its unique ring design mitigates the impact of high pole inertia, while a lightweight two-part grasping mechanism ensures secure load attachment without active force. With four primary propellers countering gravity and four auxiliary ones enhancing lateral precision, Geranos achieves comprehensive position and attitude control around hovering. Our experimental demonstration mimicking antenna/cable-car mast installations showcases Geranos ability in stacking poles (3 kg, 2 m long) with remarkable sub-5 cm placement accuracy, without the need of human manual intervention.

An alleged cartel drone attack in a remote community in the southern Mexican state of Guerrero killed 5 people, the Guerrero state prosecutor's office said. In a translated press release, the Guerrero state prosecutor's office said that the cartel attacked at least 30 people in the remote Mexican village that is plagued by cartel violence. Officials confirmed that five people were burned to death in a January 4 attack. "Through the Ministerial Investigative Police, on January 5, 2024, the first field investigations were conducted," the translated press release said. "Authorities found charred bone remains corresponding to 5 people from a burned vehicle."

The Israeli military has seemingly walked back its justification for targeting a vehicle in Gaza last week, killing two Al Jazeera journalists, United States broadcaster NBC reported. Hamza Dahdouh, the eldest son of Al Jazeera's Gaza bureau chief Wael Dahdouh, was killed in an Israeli missile strike on Sunday in Khan Younis, southern Gaza. Journalist Mustafa Thuraya was also killed in the attack, while a third passenger, journalist Hazem Rajab, was seriously injured. At the time of the attack, the Israeli army said it was targeting a "terrorist" in the vehicle. It confirmed in a statement that a military aircraft "identified and struck a terrorist who operated an aircraft that posed a threat to (Israeli) troops," adding that "we are aware of the reports that during the strike, two other suspects who were in the same vehicle as the terrorist were also hit".

We present CineMPC, a complete cinematographic system that autonomously controls a drone to film multiple targets recording user-specified aesthetic objectives. Existing solutions in autonomous cinematography control only the camera extrinsics, namely its position, and orientation. In contrast, CineMPC is the first solution that includes the camera intrinsic parameters in the control loop, which are essential tools for controlling cinematographic effects like focus, depth-of-field, and zoom. The system estimates the relative poses between the targets and the camera from an RGB-D image and optimizes a trajectory for the extrinsic and intrinsic camera parameters to film the artistic and technical requirements specified by the user. The drone and the camera are controlled in a nonlinear Model Predicted Control (MPC) loop by re-optimizing the trajectory at each time step in response to current conditions in the scene. The perception system of CineMPC can track the targets' position and orientation despite the camera effects. Experiments in a photorealistic simulation and with a real platform demonstrate the capabilities of the system to achieve a full array of cinematographic effects that are not possible without the control of the intrinsics of the camera. Code for CineMPC is implemented following a modular architecture in ROS and released to the community.

Motivated by the increasing use of quadrotors for payload delivery, we consider a joint trajectory generation and feedback control design problem for a quadrotor experiencing aerodynamic wrenches. Unmodeled aerodynamic drag forces from carried payloads can lead to catastrophic outcomes. Prior work model aerodynamic effects as residual dynamics or external disturbances in the control problem leading to a reactive policy that could be catastrophic. Moreover, redesigning controllers and tuning control gains on hardware platforms is a laborious effort. In this paper, we argue that adapting the trajectory generation component keeping the controller fixed can improve trajectory tracking for quadrotor systems experiencing drag forces. To achieve this, we formulate a drag-aware planning problem by applying a suitable relaxation to an optimal quadrotor control problem, introducing a tracking cost function which measures the ability of a controller to follow a reference trajectory. This tracking cost function acts as a regularizer in trajectory generation and is learned from data obtained from simulation. Our experiments in both simulation and on the Crazyflie hardware platform show that changing the planner reduces tracking error by as much as 83%. Evaluation on hardware demonstrates that our planned path, as opposed to a baseline, avoids controller saturation and catastrophic outcomes during aggressive maneuvers.

Walmart is expanding its drone delivery program from one pocket of the Dallas-Fort Worth area to millions of people in 30 municipalities in the area, Chief Executive Doug McMillon announced Tuesday at CES 2024. The retailer will use drone delivery systems operated by startup Zipline and by Alphabet subsidiary Wing, companies that have made hundreds of thousands of deliveries in recent years. They each recently obtained FAA clearance to fly their drones beyond visual line of sight (BVLOS) -- in other words, out of the eyesight of a human operator -- which makes large-scale drone delivery operations more practical and economical. Delivery drones offer fast service, with Walmart packages arriving between 10 and 30 minutes after an order is placed from stores up to 10 miles away. Walmart touts the technology for people who need missing cooking ingredients, last-minute birthday gifts, over-the-counter medications or movie night snacks.