Emesent Hovermap uses Simultaneous Localization and Mapping (SLAM) technology to understand its location and generate accurate point clouds. Following years of research into drone autonomy, Emesent’s SLAM algorithm is highly reliable in challenging underground and other GPS-denied settings. However, SLAM differs from tripod-based laser scanning in that the captured results are more dependent on identifying features in the environment.
Tunnels and culverts are some of the most challenging environments for SLAM scanners. The lack of features in these environments can lead to SLAM slipping, resulting in an inconsistent point cloud that may be unusable for its end purpose.
In this post, we’ll walk you through scanning a challenging culvert and point out some tips to ensure that you get the best result every time. If you’d like to view the point cloud we used in this guide, you can download the dataset here.