Abstract
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.
Abstract (translated)
视觉里程计技术通常依赖于从一系列图像中提取特征，随后计算光流。这种连续两帧之间的一点对一点的对应关系难以计算且准确性不一，这会影响里程估计的质量。为克服由于对应问题带来的困难，人们采用线性特征并融合其他传感器（如事件相机、IMU）来提升性能的方法，其中很多方法仍然严重依赖于对应的准确性。 当摄像机在移动过程中观察到一条直线时，该直线的图像会在图像时间空间中扫过一个平滑的表面。这是一个可展曲面，对其形状的分析可以提供有关里程计的信息。此外，这种估计只需要从点到线关联中的差分计算更新。 受事件相机对边缘检测优势的启发，这项研究提出了一种新的算法，用于从这些可展表面上重建3D场景和视觉里程信息。通过使用机载IMU传感器提供的惯性测量数据来约束该表面，可以极大地减少解空间的维度。
URL
https://arxiv.org/abs/2512.00327
