On-Manifold Preintegration for Real-Time Visual-Inertial Odometry.pdf

Abstract: Current approaches for visual-inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further emphasized by the fact that inerti

CNN-SLAM_ Real-Time Dense Monocular SLAM With Learned Depth Prediction.pdf

一篇结合CNN与SLAM的论文，使用CNN替换了SLAM中的几个模块，所得数据比纯几何SLAM要好，值得一读，翻译我放在了我博客中，仅供参考。

Monocular Visual-Inertial State Estimation With Online Initialization and Camera

Monocular Visual-Inertial State Estimation With Online Initialization and Camera-IMU Extinsic Calibration

CNN-SLAM Real-time dense monocular SLAM with learned depth prediction

一篇slam相关论文，结合了深度学习。用CNN单帧预测深度，可以解决单目slam中尺度不确定性、纯旋转、低纹理区域等问题。

Tightly-Coupled Monocular Visual-Inertial Fusion for AutonomousFlight

Tightly-Coupled Monocular Visual-Inertial Fusion for AutonomousFlight of Rotorcraft MAVs

Keyframe-Based Visual-Inertial Odometry Using Nonlinear Optimization.pdf

Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate Visual-Inertial Odometry or Simultaneous Localization and Mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that non-linear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual-inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochasic cloning sliding-window filter. This competititve reference implementation performs tightly-coupled filtering-based visual-inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy.

Visual-Inertial Monocular SLAM with Map Reuse.pdf

Abstract— In recent years there have been excellent results in Visual-Inertial Odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However these approaches lack the capability to close loops, and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this work we present a novel tightly-coupled Visual-Inertial Simultaneous Localization and Mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of 1% and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.

Fast_and_Robust_Monocular_Visua-Inertial_Odometry_.pdf

Fast_and_Robust_Monocular_Visua-Inertial_Odometry_.pdf Fast_and_Robust_Monocular_Visua-Inertial_Odometry_.pdf Fast_and_Robust_Monocular_Visua-Inertial_Odometry_.pdf

ORB-SLAM_ a Versatile and Accurate Monocular SLAM System.pdf

ORB-SLAM_ a Versatile and Accurate Monocular SLAM System 原版pdf

icra2017 PLSLAM论文

icra2017 PLSLAM论文 PL-SLAM: Real-Time Monocular Visual SLAM with Points and Lines

Robust Visual Inertial Odometry Using a Direct EKF-Based Approach.pdf

Abstract— In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse- distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).

Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments

In this paper, we present a novel method to fuse observations from an inertial measurement unit (IMU) and visual sensors, such that initial conditions of the inertial integration, including gravity estimation, can be recovered quickly and in a linear manner, thus removing any need for special initialization procedures. The algorithm is implemented using a graphical simultaneous localization and mapping like approach that guarantees constant time output. This paper discusses the technical aspects of the work, including observability and the ability for the system to estimate scale in real time. Results are presented of the system, estimating the platforms position, velocity, and attitude, as well as gravity vector and sensor alignment and calibration on-line in a built environment. This paper discusses the system setup, describing the real-time integration of the IMU data with either stereo or monocular vision data. We focus on human motion for the purposes of emulating high-dynamic motion, as well as to provide a localization system for future human–robot interactio

视觉知觉使用单目相机(Visual-Perception-Using-Monocular-Camera)

matlab 自动驾驶工具箱相机ADAS模块中文说明文档——视觉知觉使用单目相机(Visual-Perception-Using-Monocular-Camera)

虚拟现实 数据手套

Data glove is widely used interface device which measures hand posture (finger joint angles) and inputs it into a computer in virtual reality field. But it dose not spread throughout home because of expensive interface. On the other hand, researches to recognize a hand posture from photo/video images are performed, which are a kind of hand posture measurement system and called Vision Based Data Glove (VBDG) in recent years. In this paper, we propose a new VBDG system. It estimates hand motion using detected fingertip positions with monocular camera and inverse kinematics. However it cannot estimate hand motion when a fingertip is undetectable with selfocclusion. So our system estimates hidden finger motion, then estimates hand motion. Our experimental results show that the proposed method can estimate it with sufficient accuracy in real-time. Since camera base system is inexpensive, it fits personal use.

CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System

We present a real-time feature-based SLAM (Simultaneous Localization and Mapping) system for fisheye cameras featured by a large field-of-view (FoV). Large FoV cameras are beneficial for large-scale outdoor SLAM applications, because they increase visual overlap between consecutive frames and capture more pixels belonging to the static parts of the environment. However, current feature-based SLAM systems such as PTAM and ORB-SLAM limit their camera model to pinhole only. To compensate for the vacancy, we propose a novel SLAM system with the cubemap model that utilizes the full FoV without introducing distortion from the fisheye lens, which greatly benefits the feature matching pipeline. In the initialization and point triangulation stages, we adopt a unified vector-based representation to efficiently handle matches across multiple faces, and based on this representation we propose and analyze a novel inlier checking metric. In the optimization stage, we design and test a novel multi-pinhole reprojection error metric that outperforms other metrics by a large margin. We evaluate our system comprehensively on a public dataset as well as a self-collected dataset that contains real-world challenging sequences. The results suggest that our system is more robust and accurate than other feature-based fisheye SLAM approaches. The CubemapSLAM system has been released into the public domain.

ORB-SLAM a Versatile and Accurate Monocular SLAM System

ORB-SLAM是目前单目SLAM研究中效果比较好的，这篇文章介绍了一个基于ORB-SLAM的系统的框架，以及通过实验与PTAM、LSD-SLAM等的性能比较。

VINS-Mono-master.zip

VINS-Mono is a real-time SLAM framework for Monocular Visual-Inertial Systems. It uses an optimization-based sliding window formulation for providing high-accuracy visual-inertial odometry. It features efficient IMU pre-integration with bias correction, automatic estimator initialization, online ext

LSD-SLAM Large-Scale Direct Monocular SLAM.pdf

LSD-SLAM的英文原文，有英文基础的可以看一看，这篇论文是直接法的代表

ORB-SLAM2--an Open-Source SLAM System for Monocular Stereo and RGB-D Cameras

视觉SLAM经典文章，学好SLAM，前途无量，好好学哦。。