本文基于开源的VINS实现,里面用到IMU和RGB,本文扩展了框架,引入depth数据,并最终构建了RGBD inertial SLAM系统,用于地上机器人。本文主要的贡献: (1)为VINS-RGBD系统构建和实现集成depth的初始化过程。 (2)构建和实现集成depth的VIO,在仅有视觉或者视觉和IMU的场景,可以有一些提升。 (3)提供handheld,wheeled robot,...
一、在前一帧为正常(state==OK),表示前一帧正常初始化了,或者前一帧在正常跟踪。 1)在trackRefrenceKeyFrame/trackWithMotionModel的时候,如果跟踪成功,bOK为true,如果跟踪不成功,bOK为false。 A 如果bOK为false,在IMU的情况下,且当前帧的id,比上一帧重定位帧的id与IMU重置帧数之和更小,将跟踪状态设置为丢失。
3.1 运行局部定位 rosrun vins vins_node /home/xuduo/catkin_ws/src/VINS-Fusion/config/realsense/realsense_depth_imu_config.yaml 3.2 运行全局定位与稠密建图 rosrun loop_fusion loop_fusion_node /home/xuduo/catkin_ws/src/VINS-Fusion/config/realsense/realsense_depth_imu_config.yaml 3.3 录制bag文件 ...
Dynamic-VINS估计的轨迹,红线是VINS-RGBD估计的轨迹,黄线表示在数据集结束时执行回环检测。 Abstract Current simultaneous localization and mapping(SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM ...
The performance of PLD-VINS is validated on public OpenLORIS-Scene datasets and real-world experiments. Comparing with other state-of-the-art algorithms, such as ORB-SLAM2, PL-VINS, VINS-RGBD, and so on, the proposed PLD-VINS is more exact, robust and reliable. (C) 2021 Elsevier Masson...
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VINS-RGBD edited to run in Noetic environment (20.04) - VINS-RGBD-noetic/camera_model/CMakeLists.txt at master · U-AMC/VINS-RGBD-noetic
VINS-RGBDVINS-RGBD是一个基于深度学习的视觉惯性导航系统,它集成了深度相机(RGBD)和视觉传感器的数据。系统利用RGB图像和深度信息,通过实时的物体识别与图像匹配,结合IMU(惯性测量单元)数据,实现环境理解与导航。该模型着重于在室内或结构化环境中提供高精度的定位和姿态估计,适用于自动驾驶、机器人导航以及增强现实应用...
The main advantage of PLD-VINS is that it can improve the accuracy of state estimation and dense 3D mapping with an RGBD camera. Firstly, line features are added to local state estimation to improve the accuracy of relative state estimation between keyframes, which differs from most of existing...
Dynamic-VINS估计的轨迹,红线是VINS-RGBD估计的轨迹,黄线表示在数据集结束时执行回环检测。 Abstract Current simultaneous localization and mapping(SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM ...