CHENGCHENG GUO

CHENGCHENG GUO

I received the B.E. and M.E. degrees from Tongji University, Shanghai, China. My research interests include visual simultaneous localization and mapping

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Chengcheng Guo

SLAM engineer — visual SLAM, localization and mapping for autonomous driving.

Posts

Junciton Mapping

less than 1 minute read

Published:

Online Junction Mapping

Paper Reading Part2

17 minute read

Published:

LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (ICRA 2021)

Paper Reading Part1

5 minute read

Published:

Vision Localization

  1. Monocular Localization in HD Maps by Combining Semantic Segmentation and Distance Transform (IROS 2020, KIT)
  2. Road Mapping and Localization using Sparse Semantic Visual Features (ICRA 2021, Alibaba)
  3. Compact 3D Map-Based Monocular Localization Using Semantic Edge Alignment (IROS 2021, Alibaba)
  4. Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs (Sensors, 2020)
  5. Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans (2019)
  6. DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving (ECCV 2020)
  7. Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization (IROS 2019, Uber.ATG)
  8. Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways (Korean, 2018, sensors)
  9. Visual Semantic Localization based on HD Map for Autonomous Vehicles in Urban Scenarios (ICRA 2021, Huawei)
  10. HDMI-Loc:Exploiting high definition map image for precise localization vis bitwise particle filter (IROS 2020)
  11. DT-Loc: Monocular Visual Localization on HD Vector Map Using Distance Transforms of 2D Semantic Detections (IROS 2021)

Visual Localization

22 minute read

Published:

Visual Localization Based on a Prior Map

Mapping Based On Stereo Images

less than 1 minute read

Published:

This blog describes some of the methods I have personally explored for stereo mapping.

Stereo Match

16 minute read

Published:

Stereo Matching

portfolio

publications

A Coarse-to-Fine Multi-Sensor Fusion Localization Method Based on Semantic Edge Alignment [patent]

Published in , 1900

This invention relates to the technical field of autonomous driving and visual localization. Specifically, it is a coarse-to-fine multi-sensor fusion localization method based on semantic edge alignment, comprising the steps of obtaining an HD map, raw images, a consumer-grade vehicle-mounted GPS, and wheel odometry. This invention is a low-cost, primarily vision-based high-accuracy localization method that obtains robust localization results by fusing odometry and vehicle-mounted GPS information. By extracting stable semantic features from images and performing semantic edge alignment, it estimates the vehicle pose. The alignment approach does not require explicit data association between 3D map features and 2D image features; instead, it implicitly estimates the pose by minimizing the photometric residual of the projected points.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.