Geometric and Visual SLAM: The accuracy of modern handheld LiDAR scanners
TL;DR
It is concluded that while the fusion of visual and geometric SLAM is powerful, a stable RTK fix remains critical for achieving consistent surveygrade accuracy with current low-cost handheld scanners.
Abstract
Abstract. Recent handheld scanners increasingly integrate geometric (LiDAR-based) and visual (image-based) SLAM (Simultaneous Localization And Mapping), promising low-cost and flexible solutions for surveying tasks. This paper evaluates the accuracy of three such systems: the XGRIDS Lixel K1, the SHARE S20, and a Pix4D solution pairing an iPhone Pro with an Emlid Reach RX GNSS (Global Navigation Satellite System) antenna. We conducted experiments in two distinct environments: Scene 1, with continuous, high-quality RTK (Real-Time Kinematic) coverage, and Scene 2, which included an indoor trajectory resulting in a temporary loss of the RTK fix. Accuracy was validated against independent GNSS check points. In Scene 1, the Pix4D solution delivered survey-grade results, achieving a RMSE (Root Mean Square Error) below 3 cm in the X, Y , and Z directions. The XGRIDS and SHARE scanners yielded larger maximum errors, around 10 to 15 cm. In Scene 2, accuracy degraded; the Pix4D solution’s maximum error increased to approximately 12 cm , while the Share S20’s maximum error exceeded 25 cm. We conclude that while the fusion of visual and geometric SLAM is powerful, a stable RTK fix remains critical for achieving consistent surveygrade accuracy with current low-cost handheld scanners.