用单个全景光相机实现精准无漂移的位姿估计
PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment
- 基于光场投影建模，联合优化位姿与逆深度
- 在室内外场景中优于现有光场视觉里程计方法
- 无需复杂初始化，直接从光场成像获取深度先验
我们提出PRISM-VO，一种针对聚焦光场相机的纯优化式稀疏光度视觉里程计框架。其核心是新型光度光场捆绑调整，联合优化滑动窗口内的相机位姿与点的逆深度。通过结合单张光场图像的几何深度与时间多视角约束，PRISM-VO实现了精确且抗漂移的运动估计。通过显式建模光场投影，该方法可获得可靠的度量尺度重建，克服了单目SLAM的尺度模糊问题。重要的是，本方法仅依赖单一光场传感器，避免复杂初始化，深度先验直接由光场成像计算得出。实验表明，PRISM-VO在室内外场景中均优于当前最先进光场视觉里程计方法，其性能可媲美其他优化与学习基方法，并能准确可靠地恢复场景度量尺度。
原文摘要 · Abstract (English)
We introduce PRISM-VO, a novel pure optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. The core of PRISM-VO is a novel photometric plenoptic bundle adjustment which jointly optimizes camera poses and inverse depth values of points in a sliding window. By combining geometric depth from a single plenoptic image with temporal multi-view constraints, PRISM-VO achieves accurate and drift-resilient motion estimation. Through explicit modeling of the plenoptic projection, PRISM-VO provides reliable metric-scale reconstructions, overcoming the scale ambiguity of monocular SLAM algorithms. Importantly, our approach relies solely on a single plenoptic sensor and avoids complex initialization, as depth priors are computed directly from plenoptic imaging. Experiments show that PRISM-VO outperforms the current state-of-the-art plenoptic visual odometry method on indoor and outdoor scenes. The proposed approach rivals other optimization- and learning-based methods while accurately and reliably recovering a metric scale of the scene. Project page: https://prism-vo.github.io/
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