A Modular Optimization framework for Localization and mApping (MOLA)
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Updated
Oct 7, 2024 - C++
A Modular Optimization framework for Localization and mApping (MOLA)
An implementation of the SE-Sync algorithm for synchronization over the special Euclidean group.
[Prefer the newer MOLAorg/mola project] C++ framework for relative SLAM: Sparser Relative Bundle Adjustment (SRBA)
Python implementation of Graph SLAM
Simultaneous localization and mapping also commonly known in short as SLAM written in python.
Robotic Localization with SLAM on Raspberry Pi integrated with RP LIDAR A1. Point Cloud remote visualization doing using MQTT in real-time.
Maximizing algebraic connectivity for graph sparsification
A Graph SLAM Implementation with an Android Smartphone
Implementations of various Simultaneous Localization and Mapping (SLAM) algorithms using Octave / MATLAB.
Attempt to Implement GraphSlam as articulated in Girogio Grisetti's Paper "A Tutorial on Graph-Based Slam"
Basic Sparse-Cholesky Graph SLAM solver implemented in python
Landmark Detection and Tracking (SLAM) project for CVND
An implementation of Graph-based SLAM using only an onboard monocular camera. Developed as part of MSc Robotics Masters Thesis (2017) at University of Birmingham.
Landmark Detection and Tracking (SLAM) project for Udacity Computer Vision Nanodegree (CVND) program.
This project uses the rtabmap ros package to map a virtual environment - Robotics Udacity ND
Excercises and examples from the Probabilistic Robotics book by Thrun, Burgard, and Fox.
Udacity Computer Vision Projects
Implement SLAM, a robust method for tracking an object over time and mapping out its surrounding environment using elements of probability, motion models, linear algerbra.
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