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alphasense_core_manual

Manual for the Alphasense Core Development Kit.

awesome-slam

A curated list of SLAM resources

consistent_depth

We estimate dense, flicker-free, geometrically consistent depth from monocular video, for example hand-held cell phone video.

dbow2

Enhanced hierarchical bag-of-word library for C++

dbow3

Improved version of DBow2

eagleye

Precise localization based on GNSS and IMU.

imu_x_fusion

IMU + X(GNSS, VO) Fusion Localization based on ESKF

indoor-localization-with-imu

Pedestrian Dead Reckoning a.k.a. Indoor Localization using IMU (Inertial Measurement Unit)

iris

kapture

kapture is a file format as well as a set of tools for manipulating datasets, and in particular Visual Localization and Structure from Motion data.

kapture-localization

Provide mapping and localization pipelines based on kapture format

kp3d

Code for "Self-Supervised 3D Keypoint Learning for Ego-motion Estimation"

labelcloud

A lightweight tool for labeling 3D bounding boxes in point clouds.

latex_resume_template_kor

lio-sam

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

localrf

An algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video.

open_vins

An open source platform for visual-inertial navigation research.

openvslam_ros

OpenVSLAM's ROS package.

orb_slam2

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

orb_slam3

ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

potree

WebGL point cloud viewer for large datasets

pycolmap

Python bindings for COLMAP estimators

python-graphslam

Graph SLAM solver in Python

r2d2

reference-pe-and-measurements-db-for-wifi-time-based-scalable-location

The repository includes a reference Matlab code for a mobile WiFi client receiver, Kalman-Filter-based positioning engine (PE) and a database of time-delay measurements of an indoor environment. The database includes both real-measured data, and simulated time-delay data, as well as ground-truth client position information, which can be used for validation and performance analysis. The PE code is designed to process the database data format and produce estimates of the mobile WiFi receiver position within the indoor venue. The database may be used for the development, validation and performance analysis of additional positioning engines. This kind of WiFi client PE enables an unlimited number of clients to estimate the position and navigate within the Wi-Fi network coverage area. Hence, it enables so-called "Wi-Fi Scalable Location". A *.pdf whitepaper is included in the repository and explains the functionality and usage of the code and database.

sc-a-loam

Robust LiDAR SLAM

sivo

SIVO - Semantically Informed Visual Odometry and Mapping. Integrated Bayesian semantic segmentation with ORBSLAM_2 to select better features for Visual SLAM.

slambook-ko

supergluepretrainednetwork

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

tartanvo

TartanVO: A Generalizable Learning-based VO