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remotesensing2020

Improved VINS based on the adaptive covariance and adaptive M-estimator

removert

Remove then revert (IROS 2020)

renzhengfei

任正非**

reslam

RESLAM: A real-time robust edge-based SLAM system

reversible-data-hiding

Implementation of Reversible Data Hiding technique

revo

Robust Edge-based Visual Odometry (REVO)

rgb-d-slam

Work in Progress. A SLAM implementation based on plane and superquadric tracking.

rgbd-covariance-descriptors

Object classification using dictionary learning and RGB-D covariance descriptors.

rgbd-object-propsal

rgbd2full3d

Matlab codes that convert a RGBD image into a cad like model. This code is released as a part of my PhD dissertation.

rgbd_odometry

rgbd_svo

An adaptation of semi-direct visual odometry (SVO) for RGB-D cameras

rgbdcameraextrinsiccalibration

RGB-D extrinsic parameter automatic calibration based on plane detection

rgbdformatter

This repository holds matlab codes to convert rgbd datasets in the unified format (e.g. naming, dir structure)

rgbdplanedetection

RGBD plane detection and color-based plane refinement

rgbdslam_v2

RGB-D SLAM for ROS

rgbdvisualodometry

Visual odometry + ROS-Node with an RGBD-Camera base on Ivan Dryanovski et. al "Fast Visual Odometry and Mapping from RGB-D Data" and "An accurate closed-form estimate of ICP's covariance" by A. Censi.

risas

rl-gan-net

Official Repository of CVPR 2019 Paper : RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion

rl-navigation

roadmarkingextraction

A C++ Program for automatically extraction of road markings from MLS or ALS point cloud [ISPRS-A' 19]

rob-530_dvo

Final project for ROB 530 (Mobile Robotics), Team 5

robot-localization-examples

This code is associated with the paper submitted to Encyclopedia of EEE titled: Robot localization: An Introduction

robot_path_planning

Path planning of robots or UAVs to track unknown and/or uncertain targets that could be stationary or mobile.

robotics

A jupyter book draft about robotics and factor graphs

robotics-course-project

Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos

robust-visual-odometry-cuda

Visual odometry (based on image intensity) implementation on CUDA

robust3dvision

robustdensevo

A dense VO solution which perform robustly in dynamic scene

robustpclreconstruction

Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes