In this work, we tackle the problem of active camera localization, which
controls the camera movements actively to achieve an accurate camera pose. The
past solutions are mostly based on Markov Localization, which reduces the
position-wise camera uncertainty for localization. These approaches localize
the camera in the discrete pose space and are agnostic to the
localization-driven scene property, which restricts the camera pose accuracy in
the coarse scale. We propose to overcome these limitations via a novel active
camera localization algorithm, composed of a passive and an active localization
module. The former optimizes the camera pose in the continuous pose space by
establishing point-wise camera-world correspondences. The latter explicitly
models the scene and camera uncertainty components to plan the right path for
accurate camera pose estimation. We validate our algorithm on the challenging
localization scenarios from both synthetic and scanned real-world indoor
scenes. Experimental results demonstrate that our algorithm outperforms both
the state-of-the-art Markov Localization based approach and other compared
approaches on the fine-scale camera pose accuracy. Code and data are released
at this https URL

提出一种新型的主动摄像机定位算法，包括一个被动和一个主动模块，前者通过建立点对点的相机与世界的对应关系，在连续的位姿空间中优化相机位姿，后者则显式地建模场景和相机的不确定性部件以规划精准摄像机位姿估计的路径。

准确的主动相机定位

Towards Accurate Active Camera Localization

Localization, that is the estimation of a robot's location from sensor data,
is a fundamental problem in mobile robotics. This papers presents a version of
Markov localization which provides accurate position estimates and which is
tailored towards dynamic environments. The key idea of Markov localization is
to maintain a probability density over the space of all locations of a robot in
its environment. Our approach represents this space metrically, using a
fine-grained grid to approximate densities. It is able to globally localize the
robot from scratch and to recover from localization failures. It is robust to
approximate models of the environment (such as occupancy grid maps) and noisy
sensors (such as ultrasound sensors). Our approach also includes a filtering
technique which allows a mobile robot to reliably estimate its position even in
densely populated environments in which crowds of people block the robot's
sensors for extended periods of time. The method described here has been
implemented and tested in several real-world applications of mobile robots,
including the deployments of two mobile robots as interactive museum
tour-guides.

本文介绍了一种针对动态环境的 Markov 定位算法，它能够在表征机器人环境中所有位置的概率密度的基础上精确地确定机器人的位置，并且能够从弱化的环境模型和嘈杂的传感器中恢复定位。