在线且可认证正确的视觉里程计与建图
Online and Certifiably Correct Visual Odometry and Mapping
AI总结：
本文提出两种可认证的视觉感知算法，分别用于带误差界的视觉里程计和保守距离估计的建图，并在硬件上以30FPS在线运行验证。
摘要：
本文针对安全关键机器人应用中的认证感知问题，提出了两种新算法。第一种是认证视觉里程计算法，利用具有有界传感器噪声的RGBD相机，构建具有可证明误差界的视觉里程计估计。第二种是认证建图算法，使用相同的RGBD图像，构建障碍物环境的符号距离场，始终安全地低估到最近障碍物的距离。这是避免因视觉里程计漂移而产生误差所必需的。这些算法在硬件实验中得到了验证，我们展示了两种算法均以30FPS在线运行。这些方法还与最先进的里程计和建图技术进行了比较。
英文摘要：
This paper proposes two new algorithms for certified perception in safety-critical robotic applications. The first is a Certified Visual Odometry algorithm, which uses a RGBD camera with bounded sensor noise to construct a visual odometry estimate with provable error bounds. The second is a Certified Mapping algorithm which, using the same RGBD images, constructs a Signed Distance Field of the obstacle environment, always safely underestimating the distance to the nearest obstacle. This is required to avoid errors due to VO drift. The algorithms are demonstrated in hardware experiments, where we demonstrate both running online at 30FPS. The methods are also compared to state-of-the-art techniques for odometry and mapping.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
