We present PICCOLO, a simple and efficient algorithm for omnidirectional localization. Given a colored point cloud and a 360 panorama image of a scene, our objective is to recover the camera pose at which the panorama image is taken. Our pipeline works in an off-the-shelf manner with a single image given as a query and does not require any training of neural networks or collecting ground-truth poses of images. Instead, we match each point cloud color to the holistic view of the panorama image with gradient-descent optimization to find the camera pose. Our loss function, called sampling loss, is point cloud-centric, evaluated at the projected location of every point in the point cloud. In contrast, conventional photometric loss is image-centric, comparing colors at each pixel location. With a simple change in the compared entities, sampling loss effectively overcomes the severe visual distortion of omnidirectional images, and enjoys the global context of the 360 view to handle challenging scenarios for visual localization. PICCOLO outperforms existing omnidirectional localization algorithms in both accuracy and stability when evaluated in various environments. Code is available at \url{https://github.com/82magnolia/panoramic-localization/}.

翻译：我们提出PICCOLO，一种简单高效的全向定位算法。给定场景的彩色点云与360度全景图像，目标是恢复拍摄该全景图像时的相机位姿。本方法可直接使用单张查询图像进行定位，无需训练神经网络或收集图像的真实位姿。我们通过梯度下降优化，将点云颜色与全景图像的整体视角进行匹配，从而求解相机位姿。所提出的损失函数——采样损失——以点云为中心，对点云中每个点的投影位置进行评估。相比之下，传统光度损失以图像为中心，分别在每个像素位置比较颜色。通过简单调整比较对象，采样损失有效克服了全向图像的严重视觉畸变，并利用360度视角的全局上下文信息，处理视觉定位中的挑战性场景。在多种环境下的评估表明，PICCOLO在精度与稳定性方面均优于现有全向定位算法。代码开源地址：\url{https://github.com/82magnolia/panoramic-localization/}。