In the last decade, numerous supervised deep learning approaches requiring
large amounts of labeled data have been proposed for visual-inertial odometry
(VIO) and depth map estimation. To overcome the data limitation,
self-supervised learning has emerged as a promising alternative, exploiting
constraints such as geometric and photometric consistency in the scene. In this
study, we introduce a novel self-supervised deep learning-based VIO and depth
map recovery approach (SelfVIO) using adversarial training and self-adaptive
visual-inertial sensor fusion. SelfVIO learns to jointly estimate 6
degrees-of-freedom (6-DoF) ego-motion and a depth map of the scene from
unlabeled monocular RGB image sequences and inertial measurement unit (IMU)
readings. The proposed approach is able to perform VIO without the need for IMU
intrinsic parameters and/or the extrinsic calibration between the IMU and the
camera. estimation and single-view depth recovery network. We provide
comprehensive quantitative and qualitative evaluations of the proposed
framework comparing its performance with state-of-the-art VIO, VO, and visual
simultaneous localization and mapping (VSLAM) approaches on the KITTI, EuRoC
and Cityscapes datasets. Detailed comparisons prove that SelfVIO outperforms
state-of-the-art VIO approaches in terms of pose estimation and depth recovery,
making it a promising approach among existing methods in the literature.

本文提出了一种基于自适应视觉惯性传感器融合和对抗训练的自监督深度学习方法 (SelfVIO)，用于从未标记的单目 RGB 图像序列和惯性测量单元 (IMU) 读数中共同估计 6 自由度自我动作和景深图。该方法在 KITTI、EuRoC 和 Cityscapes 数据集上的表现优于目前研究领域中其他的 VIO 方法。

SelfVIO：自监督深度单目视觉 - 惯性里程计和深度估计

SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and  Depth Estimation

We provide an open-source C++ library for real-time metric-semantic
visual-inertial Simultaneous Localization And Mapping (SLAM). The library goes
beyond existing visual and visual-inertial SLAM libraries (e.g., ORB-SLAM,
VINS- Mono, OKVIS, ROVIO) by enabling mesh reconstruction and semantic labeling
in 3D. Kimera is designed with modularity in mind and has four key components:
a visual-inertial odometry (VIO) module for fast and accurate state estimation,
a robust pose graph optimizer for global trajectory estimation, a lightweight
3D mesher module for fast mesh reconstruction, and a dense 3D metric-semantic
reconstruction module. The modules can be run in isolation or in combination,
hence Kimera can easily fall back to a state-of-the-art VIO or a full SLAM
system. Kimera runs in real-time on a CPU and produces a 3D metric-semantic
mesh from semantically labeled images, which can be obtained by modern deep
learning methods. We hope that the flexibility, computational efficiency,
robustness, and accuracy afforded by Kimera will build a solid basis for future
metric-semantic SLAM and perception research, and will allow researchers across
multiple areas (e.g., VIO, SLAM, 3D reconstruction, segmentation) to benchmark
and prototype their own efforts without having to start from scratch.

提供一种开源的 C++ 库，为实时度量 - 语义视觉 - 惯性同时定位和制图 (SLAM) 提供支持，通过使得网格重建和三维语义标记成为可能，超越了现有的视觉和视觉惯性 SLAM 库（如 ORB-SLAM、VINS-Mono、OKVIS、ROVIO），为未来的度量 - 语义 SLAM 和感知研究奠定了坚实的基础。