Abstract
Abstract. A number of low-cost, small form factor, high resolution lidar sensors have recently been commercialized in an effort to fill the growing needs for lidar sensors on autonomous vehicles. These lidar sensors often report performance as range precision and angular accuracy, which are insufficient to characterize the overall quality of the point clouds returned by these sensors. Herein, a detailed geometric accuracy analysis of two representative autonomous sensors, the Ouster OSI-64 and the Livox Mid-40, is presented. The scanners were analyzed through a rigorous least squares adjustment of data from the two sensors using planar surface constraints. The analysis attempts to elucidate the overall point cloud accuracy and presence of systematic errors for the sensors over medium (< 40 m) ranges. The Livox Mid-40 sensor performance appears to be in conformance with the product specifications, with a ranging accuracy of approximately 2 cm. No significant systematic geometric errors were found in the acquired Mid-40 point clouds. The Ouster OSI-64 did not perform to the manufacturer specifications, with a ranging accuracy of 5.6 cm, which is nearly twice that stated by the manufacturer. Several of the individual lasers within the OSI-64’s bank of 64 lasers exhibited higher range noise than their counterparts, and examination of the residuals indicate a possible systematic error correlated with the horizontal encoder angle. This suggests that the Ouster laser may benefit from additional geometric calibration. Finally, both sensors suffered from an inability to accurately resolve edges and smaller features such as posts due to their large laser beam divergences.
Highlights
There has been an explosion of small form factor, low-cost lidar units commercially available over the past several years
This growth has primarily been a result of the autonomous vehicle market and the need for small and cheap sensors suitable for providing real-time 3D situational awareness
A variety of these low-cost laser scanners have been integrated into unmanned aerial vehicles (UAV), indoor mapping platforms, and autonomous vehicle designs as the primary mapping device for providing obstacle detection and avoidance, e.g., (Wang et al, 2017) and (Asvadi et al, 2016)
Summary
There has been an explosion of small form factor, low-cost lidar units commercially available over the past several years. A variety of these low-cost laser scanners have been integrated into unmanned aerial vehicles (UAV), indoor mapping platforms, and autonomous vehicle designs as the primary mapping device for providing obstacle detection and avoidance, e.g., (Wang et al, 2017) and (Asvadi et al, 2016) Beyond situational awareness, these devices are being routinely employed as primary data acquisition sensors for high resolution surveying and mapping (Lin et al, 2019, Elaksher et al, 2017). A detailed analysis of the sensors in a wellcontrolled environment is required to determine base observational noise levels and the possible presence of systematic errors in the resultant 3D point cloud. This analysis is fundamental to understanding the capabilities of these sensors for 3D modelling and mapping as well as autonomous vehicle navigation applications. This type of detailed analysis has only been performed for Velodyne sensors, for example (Glennie, Lichti, 2010, Glennie et al, 2016)
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