利用惯性约束的直线运动轨迹，实现无需特征匹配的视觉里程计。
Odometry Without Correspondence from Inertially Constrained Ruled Surfaces
- 通过相机拍摄直线运动形成的图像曲面重建3D场景
- 结合IMU数据降低解空间维度，提升估计精度
- 适合低光照或纹理缺失场景下的实时定位
视觉里程计通常依赖图像序列中的特征提取和光流计算来建立帧间对应关系，该过程计算成本高且精度易受干扰。尽管已有研究尝试采用线特征或融合事件相机、IMU等传感器以缓解对应问题，但多数仍依赖特征匹配。当相机移动时，若观察到一条直线，其在图像时空中的投影会形成平滑的直纹面。分析该曲面形状可提供里程计信息，且仅需基于点到线关联的微分更新即可估计。受事件相机对边缘敏感特性的启发，本文提出一种新算法，从这些直纹面中重构三维场景并实现视觉里程计。通过使用机载IMU提供的惯性测量值对曲面进行约束，显著降低了求解空间的维度。
原文摘要 · Abstract (English)
Visual odometry techniques typically rely on feature extraction from a sequence of images and subsequent computation of optical flow. This point-to-point correspondence between two consecutive frames can be costly to compute and suffers from varying accuracy, which affects the odometry estimate's quality. Attempts have been made to bypass the difficulties originating from the correspondence problem by adopting line features and fusing other sensors (event camera, IMU) to improve performance, many of which still heavily rely on correspondence. If the camera observes a straight line as it moves, the image of the line sweeps a smooth surface in image-space time. It is a ruled surface and analyzing its shape gives information about odometry. Further, its estimation requires only differentially computed updates from point-to-line associations. Inspired by event cameras' propensity for edge detection, this research presents a novel algorithm to reconstruct 3D scenes and visual odometry from these ruled surfaces. By constraining the surfaces with the inertia measurements from an onboard IMU sensor, the dimensionality of the solution space is greatly reduced.
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