Aerial 3D Mapping with Continuous Time ICP for Urban Search and Rescue

Fast reconnaissance is essential for strategic decisions during the immediate response phase of urban search and rescue missions. Nowadays, UAVs with their advantageous overview perspective are increasingly used for reconnaissance besides manual inspection of the scenario. However, data evaluation is often limited to visual inspection of images or video footage. We present our LiDAR-based aerial 3D mapping system, providing real-time maps of the environment. UAV-borne laser scans typically offer a reduced field of view. Moreover, UAV trajectories are more flexible and dynamic compared to those of ground vehicles, for which SLAM systems are often designed. We address these challenges by a two-step registration approach based on continuous time ICP. The experiments show that the resulting maps accurately represent the environment

Evaluation of a Backpack-Mounted 3D Mobile Scanning System

Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario

Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration

Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences