We present COMO, a real-time monocular mapping and odometry system that
encodes dense geometry via a compact set of 3D anchor points. Decoding anchor
point projections into dense geometry via per-keyframe depth covariance
functions guarantees that depth maps are joined together at visible anchor
points. The representation enables joint optimization of camera poses and dense
geometry, intrinsic 3D consistency, and efficient second-order inference. To
maintain a compact yet expressive map, we introduce a frontend that leverages
the covariance function for tracking and initializing potentially visually
indistinct 3D points across frames. Altogether, we introduce a real-time system
capable of estimating accurate poses and consistent geometry.

COMO 系统是一个实时单目建图和里程计系统，通过一组三维锚点来编码密集几何信息，通过锚点投影解码密集几何，保证深度图在可见锚点处完整连接，该表示允许相机位姿和密集几何的联合优化，实现内在的三维一致性和高效的二阶推断，并引入一个前端来跟踪和初始化可能在帧间视觉上无法区分的三维点，整体上，提出了一个能够估计准确位姿和一致几何的实时系统。

COMO：紧凑映射与里程计

COMO: Compact Mapping and Odometry

The ability to estimate rich geometry and camera motion from monocular
imagery is fundamental to future interactive robotics and augmented reality
applications. Different approaches have been proposed that vary in scene
geometry representation (sparse landmarks, dense maps), the consistency metric
used for optimising the multi-view problem, and the use of learned priors. We
present a SLAM system that unifies these methods in a probabilistic framework
while still maintaining real-time performance. This is achieved through the use
of a learned compact depth map representation and reformulating three different
types of errors: photometric, reprojection and geometric, which we make use of
within standard factor graph software. We evaluate our system on trajectory
estimation and depth reconstruction on real-world sequences and present various
examples of estimated dense geometry.

该研究论文利用学习到的紧凑深度图表示和重构三种不同类型的误差，将光度误差，重投影误差和几何误差应用于标准因子图软件中，将不同的方法统一到概率框架中，实现实时性能的同时对现实世界序列的轨迹估计和深度重建进行评估，并呈现了估计的丰富几何示例。

DeepFactors：实时概率稠密单目 SLAM

DeepFactors: Real-Time Probabilistic Dense Monocular SLAM

Manipulation planning is the task of computing robot trajectories that move a
set of objects to their target configuration while satisfying physically
feasibility. In contrast to existing works that assume known object templates,
we are interested in manipulation planning for a category of objects with
potentially unknown instances and large intra-category shape variation. To
achieve it, we need an object representation with which the manipulation
planner can reason about both the physical feasibility and desired object
configuration, while being generalizable to novel instances. The widely-used
pose representation is not suitable, as representing an object with a
parameterized transformation from a fixed template cannot capture large
intra-category shape variation. Hence, we propose a new hybrid object
representation consisting of semantic keypoint and dense geometry (a point
cloud or mesh) as the interface between the perception module and motion
planner. Leveraging advances in learning-based keypoint detection and shape
completion, both dense geometry and keypoints can be perceived from raw sensor
input. Using the proposed hybrid object representation, we formulate the
manipulation task as a motion planning problem which encodes both the object
target configuration and physical feasibility for a category of objects. In
this way, many existing manipulation planners can be generalized to categories
of objects, and the resulting perception-to-action manipulation pipeline is
robust to large intra-category shape variation. Extensive hardware experiments
demonstrate our pipeline can produce robot trajectories that accomplish tasks
with never-before-seen objects.

该论文研究了在存在大量形状差异的物体类别中，如何使用高效的物体表示方式和机器学习方法，实现基于感知输入的机器人运动规划，来完成任务。