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Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR-camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.
RESUMO
Background: Allograft lung ischemia-reperfusion injury (ALIRI) is a major cause of early primary graft dysfunction and poor long-term survival after lung transplantation (LTx); however, its pathogenesis has not been fully elucidated. Cell death is a mechanism underlying ALIRI. Cuproptosis is a recently discovered form of programmed cell death. To date, no studies have been conducted on the mechanisms by which cuproptosis-related genes (CRGs) regulate ALIRI. Therefore, we explored the potential biomarkers related to cuproptosis to provide new insights into the treatment of ALIRI. Materials and methods: Datasets containing pre- and post-LTx lung biopsy samples and CRGs were obtained from the GEO database and previous studies. We identified differentially expressed CRGs (DE-CRGs) and performed functional analyses. Biomarker genes were selected using three machine learning algorithms. The ROC curve and logistic regression model (LRM) of these biomarkers were constructed. CIBERSORT was used to calculate the number of infiltrating immune cells pre- and post-LTx, and the correlation between these biomarkers and immune cells was analyzed. A competing endogenous RNA network was constructed using these biomarkers. Finally, the biomarkers were verified in a validation set and a rat LTx model using qRT-PCR and Western blotting. Results: Fifteen DE-CRGs were identified. GO analysis revealed that DE-CRGs were significantly enriched in the mitochondrial acetyl-CoA biosynthetic process from pyruvate, protein lipoylation, the tricarboxylic acid (TCA) cycle, and copper-transporting ATPase activity. KEGG enrichment analysis showed that the DE-CRGs were mainly enriched in metabolic pathways, carbon metabolism, and the TCA cycle. NFE2L2, NLRP3, LIPT1, and MTF1 were identified as potential biomarker genes. The AUC of the ROC curve for each biomarker was greater than 0.8, and the LRM provided an excellent classifier with an AUC of 0.96. These biomarkers were validated in another dataset and a rat LTx model, which exhibited good performance. In the CIBERSORT analysis, differentially expressed immune cells were identified, and the biomarkers were associated with the immune cells. Conclusion: NFE2L2, NLRP3, LIPT1, and MTF1 may serve as predictors of cuproptosis and play an important role in the pathogenesis of cuproptosis in ALIRI.