The decomposition of a body is influenced by burial conditions, making it crucial to understand the impact of different conditions for accurate grave detection. Geophysical techniques using drones have gained popularity in locating clandestine graves, offering non-invasive methods for detecting surface and subsurface irregularities. Ground-penetrating radar (GPR) is an effective technology for identifying potential grave locations without disturbance. This research aimed to prototype a drone system integrating GPR to assist in grave localization and to develop software for data management. Initial experiments compared GPR with other technologies, demonstrating its valuable applicability. It is suitable for various decomposition stages and soil types, although certain soil compositions have limitations. The research used the DJI M600 Pro drone and a drone-based GPR system enhanced by the real-time kinematic (RTK) global positioning system (GPS) for precision and autonomy. Tests with simulated graves and cadavers validated the system’s performance, evaluating optimal altitude, speed, and obstacle avoidance techniques. Furthermore, global and local planning algorithms ensured efficient and obstacle-free flight paths. The results highlighted the potential of the drone-based GPR system in locating clandestine graves while minimizing disturbance, contributing to the development of effective tools for forensic investigations and crime scene analysis.

Operating in extreme environments is often challenging due to the lack of perceptual knowledge. During fire incidents in large buildings, the extreme levels of smoke can seriously impede a firefighter’s vision, potentially leading to severe material damage and loss of life. To increase the safety of firefighters, research is conducted in collaboration with Dutch fire departments into the usability of Unmanned Ground Vehicles to increase situational awareness in hazardous environments. This paper proposes FirebotSLAM, the first algorithm capable of coherently computing a robot’s odometry while creating a comprehensible 3D map solely using the information extracted from thermal images. The literature showed that the most challenging aspect of thermal Simultaneous Localization and Mapping (SLAM) is the extraction of robust features in thermal images. Therefore, a practical benchmark of feature extraction and description methods was performed on datasets recorded during a fire incident. The best-performing combination of extractor and descriptor is then implemented into a state-of-the-art visual SLAM algorithm. As a result, FirebotSLAM is the first thermal odometry algorithm able to perform global trajectory optimization by detecting loop closures. Finally, FirebotSLAM is the first thermal SLAM algorithm to be tested in a fiery environment to validate its applicability in an operational scenario.

This letter introduces a novel approach for controlling aerial robots during physical interaction by integrating Admittance Control with Nonlinear Model Predictive Control (NMPC). Unlike existing methods, our technique incorporates the desired impedance dynamics directly into the NMPC prediction model, alongside the robot's dynamics. This allows for the explicit prediction of how the robot's impedance will respond to interaction forces within the prediction horizon. Consequently, our controller effectively tracks the desired impedance behavior during physical interaction while seamlessly transitioning to trajectory tracking in free motion, all while consistently respecting actuator constraints. The efficacy of this method is validated through real-time simulations and experiments involving physical interaction tasks with an aerial robot. Our findings demonstrate that, across most scenarios, our method significantly outperforms the state-of-the-art (which does not predict future impedance state), achieving a reduction in tracking error of up to 90%. Furthermore, the results indicate that our approach enables smoother and safer physical interaction, characterized by reduced oscillations and the absence of the unstable behavior observed with the state-of-the-art method in certain situations.

Building on the concept of LIDAR–RGB extrinsic calibration using simple box targets (Pusztai and Hajder, 2017), we propose a low-cost and effective method for extrinsic calibration between a thermal camera and a LIDAR. Our approach utilizes a single, inverted half-box target featuring a heated asymmetric circles grid on one of its planes, designed for high visibility in thermal imagery. Thermal camera poses are estimated using OpenCV’s calibration toolbox, while LIDAR poses are computed by extracting the three planar surfaces of the calibration target. The extrinsic transformation between the thermal camera and the LIDAR is then determined using a cycle consistency constraint. We evaluate the method in terms of calibration accuracy and precision. Additionally, the setup can be used to assess the time synchronization between the thermal and LIDAR data streams by visualizing the projection errors on dynamic scene elements. Additional results can be found in the video at youtu.be/0ODHZe4rVec. The source code is available at github.com/SaxionMechatronics/CycleCameraLiDARCalibration.