The EVI-SAM system takes events, images, and IMU data as inputs to simultaneously estimate the 6-DoF pose and reconstruct the 3D dense maps of the environment.
The tracking module employs a hybrid framework that combines feature-based and direct-based methods to process events, enabling the estimation of 6-DoF pose. A sliding window graph-based optimization framework is designed to tightly fuse the event-based geometric errors, event-based photometric errors, image-based geometric errors, and IMU pre-integration.
The mapping module initially reconstructs the event-based semi-dense depth using a space-sweep approach. It then integrates the aligned intensity image as guidance to reconstruct the event-based dense depth and render the texture of the map. Finally, the TSDF-based map fusion is designed to generate a 3D global consistent texture map and surface mesh of the environment.
The proposed EVI-SAM effectively balances accuracy and robustness while maintaining computational efficiency, showcasing superior pose tracking and dense mapping performance in challenging scenarios.
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by Weipeng Guan... at arxiv.org 04-22-2024
https://arxiv.org/pdf/2312.11911.pdfDeeper Inquiries