Academic literature on the topic 'MacroDroid'

AbstractVariation in the shape of microdrops of a highly concentrated magnetic colloid resulting from phase separation in a magnetic fluid has been studied. It has been found that even weak magnetic fields (such as those comparable to the geomagnetic field) substantially influence the geometry and behavior of microdrops. Different configurations of microdrops in a rotating magnetic field have been considered. The occurrence of a rotation moment that acts on a macrodrop of a magnetic fluid in a rotating magnetic field has been shown. The rotation moment is due to the rotation of concentrated phase microdrops inside the macrodrop. The macroscopic rotation frequency of a drop’s surface as a function of the applied magnetic field frequency and strength has been measured.

Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm.

Abstract. The use of drones to explore indoor spaces has gained attention and popularity for disaster management and indoor navigation applications. In this paper we present the operations and mapping techniques of two drones that are different in terms of size, the sensors deployed, and the positioning and mapping techniques used. The first drone is a low-cost commercial quadcopter microdrone, a Crazyflie, while the second drone is a relatively expensive research quadcopter macrodrone, called MAX. We investigated their feasibility in mapping areas where satellite positioning is not available, such as indoor spaces. We compared the point clouds obtained by a multi-ranger deck, a multi-layer LIDAR scanner and a stereo camera, and assessed each against ground truth obtained with a terrestrial laser scanner. Results showed that both drones are capable of mapping relatively cluttered indoor environments and can provide point clouds that are sufficient for a quick exploration. Furthermore, the LIDAR scanner-based system can handle a relatively large office environment with an accumulated drift less than 0.02% (1 cm) on the Z-axis and 0.77% (50 cm) on the X and Y axes over a length trajectory of about 65 m. Despite the limited features of the sensor configuration of the Crazyflie, its performance is promising for mapping indoor spaces, given the relatively low deviation from the ground truth: cloud-to-cloud distances measured were generally less than 20 cm.