Visual Localization is the problem of estimating the position and orientation, i.e., the camera pose, from which an image was taken. Long-Term Visual Localization is the problem of robustly handling changes in the scene. Simultaneous Localization and Mapping (SLAM) is the problem of tracking the motion of a camera (or sensor system) while simultaneously building a (3D) map of the scene. Similarly, Visual Odometry (VO) algorithms track the motion of a sensor system, without necessarily creating a map of the scene. Localization, SLAM, and VO are highly related problems, e.g., SLAM algorithms can be used to construct maps that are later used by Localization techniques, Localization approaches can be used to detect loop closures in SLAM and SLAM / VO can be used to integrate frame-to-frame tracking into real-time Localization approaches.

Visual Localization, SLAM, and VO are all fundamental capabilities required in many Computer Vision and Robotics applications, such as Augmented / Mixed / Virtual Reality and other emerging applications based on location context, such as scene understanding, city navigation and tourist recommendation, and autonomous vehicles such as self-driving cars and other robots. Consequently, visual localization, SLAM, and VO are important research areas, both in academia and industry.

This workshop covers a wide range of topics, including, but not limited to.

Long-Term Operation of Localization and Mapping
Geometric Methods for SLAM in Dynamic Environments
Hybrid (Learning + Geometry) SLAM Systems
Semantic-context applied to SLAM, VO, and Visual Localization
Applications of SLAM, VO, and Visual Localization in challenging domains
SLAM / VO / Visual Localization Datasets, Benchmarks, and Metrics
(6DOF) Visual Localization
Place Recognition
Image Retrieval
(Deep Learned) Local Features and Matching
Deep Learning for Scene Coordinate Regression and Camera Pose Regression
3D Reconstruction for Mapping
Augmented / Mixed / Virtual Reality applications based on Visual Localization, SLAM, or VO
Applications based on Visual Localization, SLAM, or VO in the area of Robotics and Autonomous Driving
Semantic Scene Understanding for Localization and Mapping
Simultaneous Localization and Mapping
(3D) (Semantic) Scene Understanding and Scene Representations
Image-based localization and navigation
Monocular and Stereo Visual Odometry
Multi-Modal Visual Sensor Data Fusion
Real-Time Object Tracking
Deep Learning for Visual Odometry and SLAM
Large-Scale SLAM
Rendering and Visualization of Large-Scale Models
Feature Representation, Indexing, Storage, and Analysis
Object Detection and Recognition based on Location Context
Landmark Mining and Tourism Recommendation
Video Surveillance
Large-Scale Multi-Modal Datasets Collection
Visual Odometry for Night Vision
Odometry based on Event Cameras
Scale Estimation for Monocular Odometry with Prior Information
End-to-End Visual Odometry, SLAM and Localization


For questions, please refer to the program chair Prof. Guoyu Lu (luguoyu@cis.rit.edu).