论文标题
在动态环境中稳健大满贯的光场前端
A Light Field Front-end for Robust SLAM in Dynamic Environments
论文作者
论文摘要
人们普遍期望机器人应在城市环境中运行，通常由人物，家具和汽车在内的潜在动态实体组成。动态对象通过将错误引入前端来对视觉猛击算法提出挑战。本文提出了一个光场猛击前端，对动态环境非常强大。一个光场捕获了一束从空间中的点出现的光线，使我们能够通过合成孔径成像（SAI）遮挡静态背景的动态对象。我们使用语义分段检测APRIORI动态对象，并在从线性摄像机阵列中获取的光场上执行语义引导SAI。我们同时估计深度图和单个步骤中静态背景的重新关注图像，从而消除了静态场景初始化的需求。该算法的GPU实现有助于以接近4 fps的实时速度运行。我们证明，我们的方法通过将其与最先进的SLAM算法进行比较，从而提高了动态环境中姿势估计的鲁棒性和准确性。
There is a general expectation that robots should operate in urban environments often consisting of potentially dynamic entities including people, furniture and automobiles. Dynamic objects pose challenges to visual SLAM algorithms by introducing errors into the front-end. This paper presents a Light Field SLAM front-end which is robust to dynamic environments. A Light Field captures a bundle of light rays emerging from a single point in space, allowing us to see through dynamic objects occluding the static background via Synthetic Aperture Imaging(SAI). We detect apriori dynamic objects using semantic segmentation and perform semantic guided SAI on the Light Field acquired from a linear camera array. We simultaneously estimate both the depth map and the refocused image of the static background in a single step eliminating the need for static scene initialization. The GPU implementation of the algorithm facilitates running at close to real time speeds of 4 fps. We demonstrate that our method results in improved robustness and accuracy of pose estimation in dynamic environments by comparing it with state of the art SLAM algorithms.
