Asymmetric Gaussian Echo Model for LiDAR Intensity Correction
In light detection and ranging (LiDAR) applications, correct intensities from echo data intuitively contribute to the characterization of target reflectivity. However, the power in raw echo waveforms may be clipped owing to the limited dynamic range of LiDAR sensors, which directly results in false...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4625 |
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| author | Xinyue Ma Haitian Jiang Xin Jin |
| author_facet | Xinyue Ma Haitian Jiang Xin Jin |
| author_sort | Xinyue Ma |
| collection | DOAJ |
| description | In light detection and ranging (LiDAR) applications, correct intensities from echo data intuitively contribute to the characterization of target reflectivity. However, the power in raw echo waveforms may be clipped owing to the limited dynamic range of LiDAR sensors, which directly results in false intensity values generated by existing LiDAR systems working in scenarios involving highly reflective objects or short distances. To tackle the problem, an asymmetric Gaussian echo model is proposed in this paper so as to recover echo power–time curves faithfully to its optical physics. Considering the imbalance in temporal length and steepness between rising and falling edges, the echo model features a shared mean and two distinct standard deviations on both sides. The accuracy and effectiveness of the proposed model are demonstrated by correcting the power–time curve from a real LiDAR loaded with avalanche photodiode (APD) sensors and estimating the reflectivities of real targets. As when tested by targets with reflectivities from low to high placed at distances from near to far, the model achieves a maximum of 41.8-fold improvement in relative error for the same target with known reflectivity and a maximum of 36.0-fold improvement in the coefficient of variation for the same target along the whole range of 100 m. Providing accurate and stable characterization of reflectivity in different ranges, the model greatly boosts applications consisting of semantic segmentation and object recognition, such as autonomous driving and environmental monitoring. |
| format | Article |
| id | doaj-art-bd14c7a132d14581b8dab2dceddac0c4 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-bd14c7a132d14581b8dab2dceddac0c42024-12-27T14:50:43ZengMDPI AGRemote Sensing2072-42922024-12-011624462510.3390/rs16244625Asymmetric Gaussian Echo Model for LiDAR Intensity CorrectionXinyue Ma0Haitian Jiang1Xin Jin2Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaIn light detection and ranging (LiDAR) applications, correct intensities from echo data intuitively contribute to the characterization of target reflectivity. However, the power in raw echo waveforms may be clipped owing to the limited dynamic range of LiDAR sensors, which directly results in false intensity values generated by existing LiDAR systems working in scenarios involving highly reflective objects or short distances. To tackle the problem, an asymmetric Gaussian echo model is proposed in this paper so as to recover echo power–time curves faithfully to its optical physics. Considering the imbalance in temporal length and steepness between rising and falling edges, the echo model features a shared mean and two distinct standard deviations on both sides. The accuracy and effectiveness of the proposed model are demonstrated by correcting the power–time curve from a real LiDAR loaded with avalanche photodiode (APD) sensors and estimating the reflectivities of real targets. As when tested by targets with reflectivities from low to high placed at distances from near to far, the model achieves a maximum of 41.8-fold improvement in relative error for the same target with known reflectivity and a maximum of 36.0-fold improvement in the coefficient of variation for the same target along the whole range of 100 m. Providing accurate and stable characterization of reflectivity in different ranges, the model greatly boosts applications consisting of semantic segmentation and object recognition, such as autonomous driving and environmental monitoring.https://www.mdpi.com/2072-4292/16/24/4625LiDAR echoasymmetric Gaussianclipped waveformintensity correction |
| spellingShingle | Xinyue Ma Haitian Jiang Xin Jin Asymmetric Gaussian Echo Model for LiDAR Intensity Correction Remote Sensing LiDAR echo asymmetric Gaussian clipped waveform intensity correction |
| title | Asymmetric Gaussian Echo Model for LiDAR Intensity Correction |
| title_full | Asymmetric Gaussian Echo Model for LiDAR Intensity Correction |
| title_fullStr | Asymmetric Gaussian Echo Model for LiDAR Intensity Correction |
| title_full_unstemmed | Asymmetric Gaussian Echo Model for LiDAR Intensity Correction |
| title_short | Asymmetric Gaussian Echo Model for LiDAR Intensity Correction |
| title_sort | asymmetric gaussian echo model for lidar intensity correction |
| topic | LiDAR echo asymmetric Gaussian clipped waveform intensity correction |
| url | https://www.mdpi.com/2072-4292/16/24/4625 |
| work_keys_str_mv | AT xinyuema asymmetricgaussianechomodelforlidarintensitycorrection AT haitianjiang asymmetricgaussianechomodelforlidarintensitycorrection AT xinjin asymmetricgaussianechomodelforlidarintensitycorrection |