[IROS2022] Robust Real-time LiDAR-inertial Initialization Method.

Official Implementation of "Flare7K: A Phenomenological Nighttime Flare Removal Dataset"

Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment

Extending the Robustness of LiDAR SLAM to Geometrically Degenerate Scenarios

Implementation of "Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation"

A Lidar-Inertial State Estimator for Robust and Efficient Navigation based on iterated error-state Kalman filter

FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry

ROS-based autonomous navigation tools for mobile robots with 2D LiDAR

Reference implementation of the Reflectance Field Map (Foster, Johnson, Kuipers ICRA 2023)

We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

ETH-Microsoft dataset for the ICCV 2021 visual localization challenge

OneDrive Client for Linux

🚀 AirVO upgrades to AirSLAM 🚀

a Multi-Map LiDAR-Inertial System for Over-Degraded Environments

Code for the paper "A New Approach to 3D ICP Covariance Estimation for Mobile Robotics"

Full-python LiDAR SLAM using ICP and Scan Context

TartanAir dataset tools and samples

A Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry (LIVO).

The fuse stack provides a general architecture for performing sensor fusion live on a robot. Some possible applications include state estimation, localization, mapping, and calibration.

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

A Robust and Versatile Monocular Visual-Inertial State Estimator

LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

CMU 16-833 "Robot Localization and Mapping" Course Project

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

SLAM Resources to follow up current SLAM trends and papers.

Markdown vs LaTeX / TeX Markup

Generate LaTeX equations from images in your clipboard or vault

Real-Time Probabilistic Dense Monocular SLAM Using Compact Code Representation