activity
20192021
most citedIntensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment

132 citations · 210 across the 5 of their papers we have counts for

collaborators

9 papers

cs.RO20217 cited

Robust Glare Detection: Review, Analysis, and Dataset Release

Mahdi Abolfazli Esfahani, Han Wang

Sun Glare widely exists in the images captured by unmanned ground and aerial vehicles performing in outdoor environments. The existence of such artifacts in images will result in w…

cs.CV20211 cited

Visual Enhanced 3D Point Cloud Reconstruction from A Single Image

Guiju Ping, Mahdi Abolfazli Esfahani, Han Wang

Solving the challenging problem of 3D object reconstruction from a single image appropriately gives existing technologies the ability to perform with a single monocular camera rath…

cs.RO202168 cited

Lightweight 3-D Localization and Mapping for Solid-State LiDAR

Han Wang, Chen Wang, Lihua Xie

The LIght Detection And Ranging (LiDAR) sensor has become one of the most important perceptual devices due to its important role in simultaneous localization and mapping (SLAM). Ex…

cs.RO2021132 cited

Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment

Han Wang, Chen Wang, Lihua Xie

Simultaneous Localization And Mapping (SLAM) is a task to estimate the robot location and to reconstruct the environment based on observation from sensors such as LIght Detection A…

cs.CV2020

Online Visual Place Recognition via Saliency Re-identification

Han Wang, Chen Wang, Lihua Xie

As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often f…

cs.RO2020

Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection

Han Wang, Chen Wang, Lihua Xie

Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor…