Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.

ABSTRACT:The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

ABSTRACT:Accurate 3D reconstruction has become essential for non-traditional mapping applications such as urban planning, mining industry, environmental monitoring, navigation, surveillance, pipeline inspection, infrastructure monitoring, landslide hazard analysis, indoor localization, and military simulation. The needs of these applications cannot be satisfied by traditional mapping, which is based on dedicated data acquisition systems designed for mapping purposes. Recent advances in hardware and software development have made it possible to conduct accurate 3D mapping without using costly and high-end data acquisition systems. Low-cost digital cameras, laser scanners, and navigation systems can provide accurate mapping if they are properly integrated at the hardware and software levels. Unmanned Aerial Vehicles (UAVs) are emerging as a mobile mapping platform that can provide additional economical and practical advantages. However, such economical and practical requirements need navigation systems that can provide uninterrupted navigation solution. Hence, testing the performance characteristics of Micro-Electro-Mechanical Systems (MEMS) or low cost navigation sensors for various UAV applications is important research. This work focuses on studying the performance characteristics under different manoeuvres using inertial measurements integrated with single point positioning, RealTime-Kinematic (RTK), and additional navigational aiding sensors. Furthermore, the performance of the inertial sensors is tested during Global Positioning System (GPS) signal outage.

<p><strong>Abstract.</strong> The autonomous vehicles, such as wheeled robots and drones, efficiently contribute in the search and rescue operations. Specially for indoor environments, these autonomous vehicles rely on simultaneous localization and mapping approach (SLAM) to construct a map for the unknown environment and simultaneously to estimate the vehicle’s position inside this map. The result of the scan matching process, which is a key step in many of SLAM approaches, has a fundamental role of the accuracy of the map construction. Typically, local and global scan matching approaches, that utilize laser scan rangefinder, suffer from accumulated errors as both approaches are sensitive to previous history. The reference key frame (RKF) algorithm reduces errors accumulation as it decreases the dependency on the accuracy of the previous history. However, the RKF algorithm still suffers; as most of the SLAM approaches, from scale shrinking problem during scanning corridors that exceed the maximum detection range of the laser scan rangefinder. The shrinking in long corridors comes from the unsuccessful estimation of the longitudinal movement from the implemented RKF algorithm and the unavailability of this information from external source as well. This paper proposes an improvement for the RKF algorithm. This is achieved by integrating the outcomes of the optical flow with the RKF algorithm using extended Kalman filter (EKF) to overcome the shrinking problem. The performance of the proposed algorithm is compared with the RKF, iterative closest point (ICP), and Hector SLAM in corridors that exceed the maximum detection range of the laser scan rangefinder.</p>

Micro aerial vehicles (MAVs) have been acknowledged as an influential technology for indoor search and rescue operations. The time constraint is a crucial factor in most search and rescue operations. The employed MAVs in indoor environments are characterized by short endurance flight time and limited payload weights. Hence, adding more batteries to extend the flight time is practically not feasible. Typically, most of the indoor missions’ environments might not be accessed and remain unknown. Working in such environments requires effective exploration and information gathering to save time and maximize the coverage area. Furthermore, due to the dynamism of such environments, choosing the least risky trajectory is an important task. This paper proposes a real-time active exploration technique which is capable of efficiently generating paths that minimize the vehicle’s risk and maximize the coverage area. Furthermore, it accomplishes real-time monitoring of sudden changes in the estimated map, due to the dynamic objects, by reevaluating at real-time the destination and trajectory to minimize the risk on the chosen path and simultaneously preserving the maximization of the coverage area. Ultimately, recording the implemented trajectory of the vehicle also assists in time-saving as the vehicle depends on this trajectory during the exit process. The performance of the technique is studied under static and dynamic environments and is also compared with different algorithms.