In this paper, we present TANDEM a real-time monocular tracking and dense
mapping framework. For pose estimation, TANDEM performs photometric bundle
adjustment based on a sliding window of keyframes. To increase the robustness,
we propose a novel tracking front-end that performs dense direct image
alignment using depth maps rendered from a global model that is built
incrementally from dense depth predictions. To predict the dense depth maps, we
propose Cascade View-Aggregation MVSNet (CVA-MVSNet) that utilizes the entire
active keyframe window by hierarchically constructing 3D cost volumes with
adaptive view aggregation to balance the different stereo baselines between the
keyframes. Finally, the predicted depth maps are fused into a consistent global
map represented as a truncated signed distance function (TSDF) voxel grid. Our
experimental results show that TANDEM outperforms other state-of-the-art
traditional and learning-based monocular visual odometry (VO) methods in terms
of camera tracking. Moreover, TANDEM shows state-of-the-art real-time 3D
reconstruction performance.

介绍了实时单目跟踪和稠密建图框架 TANDEM，利用全局模型渲染的深度图进行密集直接图像对齐和 3D 成本聚合，融合为一致的全局地图，表现出优异的相机跟踪和 3D 重建表现。

TANDEM: 使用深度多视图立体视觉在实时中进行追踪和密集的地图构建

TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view  Stereo

We present a direct visual-inertial odometry (VIO) method which estimates the
motion of the sensor setup and sparse 3D geometry of the environment based on
measurements from a rolling-shutter camera and an inertial measurement unit
(IMU).
The visual part of the system performs a photometric bundle adjustment on a
sparse set of points. This direct approach does not extract feature points and
is able to track not only corners, but any pixels with sufficient gradient
magnitude. Neglecting rolling-shutter effects in the visual part severely
degrades accuracy and robustness of the system. In this paper, we incorporate a
rolling-shutter model into the photometric bundle adjustment that estimates a
set of recent keyframe poses and the inverse depth of a sparse set of points.
IMU information is accumulated between several frames using measurement
preintegration, and is inserted into the optimization as an additional
constraint between selected keyframes. For every keyframe we estimate not only
the pose but also velocity and biases to correct the IMU measurements. Unlike
systems with global-shutter cameras, we use both IMU measurements and
rolling-shutter effects of the camera to estimate velocity and biases for every
state.
Last, we evaluate our system on a novel dataset that contains global-shutter
and rolling-shutter images, IMU data and ground-truth poses for ten different
sequences, which we make publicly available. Evaluation shows that the proposed
method outperforms a system where rolling shutter is not modelled and achieves
similar accuracy to the global-shutter method on global-shutter data.

本文提出一种直接的视觉惯性里程计方法（VIO），该方法基于来自快门滚动摄像机和惯性测量单元（IMU）的测量，估计传感器设置的运动和环境的稀疏 3D 几何图形，并结合光度束调整，估计一组最近的关键帧姿势和稀疏点的反深度，同时利用 IMU 信息，基于滚动快门效应对速度和偏差进行校准。在公开数据集上进行的实验说明，与不考虑滚动快门的系统相比，所提出的方法表现更好，并在全局快门数据上实现了与全局快门方法类似的准确度。

基于滚动快门的直接视觉惯性里程计建模

Rolling-Shutter Modelling for Direct Visual-Inertial Odometry

Photometric bundle adjustment, PBA, accurately estimates geometry from video.
However, current PBA systems have a temporary map that cannot manage scene
reobservations. We present, DSM, a full monocular visual SLAM based on PBA. Its
persistent map handles reobservations, yielding the most accurate results up to
date on EuRoC for a direct method.

本研究针对现有 Photometric bundle adjustment 系统中无法处理场景重观测的问题，提出基于全单目视觉 SLAM 的 DSM 方案，实现了对场景重观测的持续跟踪和最精准估算，且在 EuRoC 研究中达到了迄今为止的最佳效果。