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- W3048130552 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." @default.
- W3048130552 created "2020-08-13" @default.
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- W3048130552 date "2020-08-06" @default.
- W3048130552 modified "2023-10-14" @default.
- W3048130552 title "ACCURACY ASSESSMENT AND CALIBRATION OF LOW-COST AUTONOMOUS LIDAR SENSORS" @default.
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- W3048130552 doi "https://doi.org/10.5194/isprs-archives-xliii-b1-2020-371-2020" @default.
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