Indoor and Outdoor Backpack Mapping with Calibrated Pair of Velodyne LiDARs

Abstract

This paper presents a human-carried mapping backpack based on a pair of Velodyne LiDAR scanners. Our system is a universal solution both for large scale outdoor and also smaller indoor environments. It benefits from a combination of two LiDAR scanners what makes the odometry estimation more precise. The scanners are mounted under different angles, thus larger space around the backpack is scanned. By fusion with GNSS/INS sub-system, the mapping of featureless environments and also the georeferencing of resulting point cloud is possible. By deploying SoA methods for registration and the loop closure optimization it provides sufficient precision for many applications in BIM (Building Information Modeling), inventory check, construction planning, etc. In our indoor experiments, we evaluated our proposed backpack against ZEB-1 solution, using FARO terrestrial scanner as the reference, yielding similar results in terms of precision, while our system provides higher data density, laser intensity readings, and scalability for large environments.This paper presents a human-carried mapping backpack based on a pair of Velodyne LiDAR scanners. Our system is a universal solution both for large scale outdoor and also smaller indoor environments. It benefits from a combination of two LiDAR scanners what makes the odometry estimation more precise. The scanners are mounted under different angles, thus larger space around the backpack is scanned. By fusion with GNSS/INS sub-system, the mapping of featureless environments and also the georeferencing of resulting point cloud is possible. By deploying SoA methods for registration and the loop closure optimization it provides sufficient precision for many applications in BIM (Building Information Modeling), inventory check, construction planning, etc. In our indoor experiments, we evaluated our proposed backpack against ZEB-1 solution, using FARO terrestrial scanner as the reference, yielding similar results in terms of precision, while our system provides higher data density, laser intensity readings, and scalability for large environments.