Projects sharing features with Slambook2

ndarray is a multidimensional array library for Rust that serves as a linear algebra framework and scientific computing tool. It provides the core infrastructure for creating and manipulating n-dimensional arrays, functioning as both a parallel array processor and a toolkit for numerical data analysis. The library distinguishes itself by providing efficient slicing and memory views, allowing for data sharing without copying. It leverages optimized backend math libraries for high-speed matrix multiplication and distributes heavy mathematical iterations across multiple CPU threads to accelerate

libigl is a C++ geometry processing library used for analyzing and manipulating 3D triangle and tetrahedral meshes. It functions as a numerical linear algebra suite and a mesh manipulation framework, integrating a geometric deformation engine to implement rigid and polyharmonic transformations. The project is distinguished by its header-only library design and its implementation of specialized deformation techniques, including rigid-as-possible and polyharmonic shape deformation. It also provides a visualization tool for rendering surfaces and scalar fields with interactive scene controls and

Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision operations as differentiable tensors to enable integration into deep learning pipelines and supports the transpilation of operations across PyTorch, TensorFlow, JAX, and NumPy. The project provides specialized toolsets for geometric vision and stereo depth, including algorithms for 3D scene reconstruction, camera calibration, and pose estimation. It further distinguishes itself as a differentiable image augmentation framework, applying random geometric and color transformations w

This project is a collection of foundational machine learning algorithms and data science tools implemented in Python. It focuses on building the logic of these tools using basic programming primitives rather than relying on specialized libraries. The implementation covers several core domains, including a linear algebra library for matrix and vector operations, a statistical analysis toolkit for probability and hypothesis testing, and a framework for map-reduce distributed processing. It also includes implementations for natural language processing, graph theory for network analysis, and var

numbers.js is a comprehensive mathematics library for JavaScript that provides a collection of advanced functions for scientific computing and data analysis. It is designed to handle complex mathematical operations through a modular architecture, offering tools for calculus, statistics, linear algebra, and prime number analysis. The library distinguishes itself by providing explicit control over numerical precision, allowing users to define error thresholds and manage decimal accuracy to mitigate rounding discrepancies. This focus on precision is paired with a suite of computational tools tha

Eloquent-JavaScript is a comprehensive JavaScript programming textbook and interactive coding tutorial designed for web development education. It serves as both a language reference and a practical guide, combining theoretical lessons with an environment where learners can execute and modify code examples. The project focuses on the fundamental principles of the JavaScript language, including lexical scoping, prototype-based inheritance, and asynchronous patterns. It provides detailed instruction on object-oriented programming, functional programming, and the use of the browser DOM to create

This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Slambook is a visual SLAM framework designed for simultaneous localization and mapping. It provides an integrated system to estimate camera motion and reconstruct 3D environments using visual sensor data. The project includes a visual odometry engine to track camera movement and a dense 3D reconstruction tool for creating volumetric representations of scenes. It features a loop closure detection system to recognize previously visited locations and a pose graph optimizer to refine trajectories and ensure global map consistency. The framework covers spatial estimation and environment modeling

ORB_SLAM3 is a visual-inertial SLAM library designed for real-time simultaneous localization and mapping. It provides a framework for tracking camera movement and building 3D maps of environments using monocular, stereo, or RGB-D cameras combined with inertial sensors. The system features a multi-map fusion engine capable of merging separate spatial sessions into a single seamless representation of an environment. It includes specialized processing for wide-angle and fisheye lenses to expand the visual field of view for spatial tracking. The library covers a broad range of spatial intelligen

pyslam is a framework for Simultaneous Localization and Mapping that combines Python flexibility with C++ performance. It is a sparse SLAM implementation designed to map environment geometry and track device location by processing image frames into 3D points. The project features a bridge for exposing high-performance C++ classes to Python scripts using zero-copy memory sharing. This integration allows for switching between a scripting interface for rapid prototyping and a compiled core for execution speed. The system includes a spatial map optimizer to refine 3D point and camera pose estima

ORB_SLAM2 is a visual simultaneous localization and mapping system that tracks camera movement and builds 3D environments from image data. It functions as a real-time visual odometry tool and sparse 3D reconstructor, computing the position and orientation of a camera while generating a point cloud map of a physical space. The system utilizes a camera relocalization engine to identify a camera's position within a known map after tracking failure or system restarts. It incorporates a spatial tracker to enable the precise insertion and composition of virtual 3D objects into real-world planar reg

This repository is a C++ study collection and programming courseware designed to support a structured language curriculum. It functions as a comprehensive study repository containing conceptual notes and verified exercise solution sets for learning the C++ programming language. The project provides a set of working source code implementations and answers for practical programming problems. These materials enable language learning and programming practice by allowing users to implement code and verify their logic against solved exercises. The content is organized into a chapter-based director

Open_vins is a visual-inertial odometry framework and SLAM system designed for robotic state estimation. It uses an Extended Kalman Filter to fuse high-frequency inertial sensor data with visual feature tracks to estimate the position and orientation of a moving device. The system features a sensor calibration suite for calculating intrinsic and extrinsic parameters, as well as temporal offsets between cameras and inertial measurement units. It includes a manifold interpolator that uses B-Spline curves over the special Euclidean group to produce smooth trajectory paths between discrete pose e

This repository is a comprehensive collection of fully worked solutions to exercises and problems from the standard algorithms textbook by Cormen, Leiserson, Rivest, and Stein (CLRS). It serves as an educational reference for algorithm design and analysis, providing step-by-step reasoning, pseudocode, and mathematical proofs for a wide range of topics. The content spans core computer science areas: algorithm analysis with asymptotic notation, recurrence solving, and amortized cost analysis; data structure implementation and operations for binary search trees, red-black trees, B-trees, Fibonac

This project is a machine learning textbook companion and code reference that translates theoretical statistical learning exercises into executable implementations. It serves as a programmatic study guide for implementing foundational machine learning algorithms and solving structured data problems. The repository provides predictive modeling notebooks that combine narrative explanations with code to derive and validate statistical algorithms. These implementations are available as a reference for both Python and R, utilizing the Scikit-Learn API for model fitting and prediction. The codebas

OM1 is a multimodal AI agent runtime and orchestration framework designed to connect large language models to physical robot hardware and sensors. It provides an execution environment that processes audio, video, and sensor data to drive autonomous decisions and actions in real-world settings. The system integrates a robotics SLAM and navigation stack with a hardware abstraction layer, allowing high-level AI commands to be translated into low-level motor and actuator instructions. It distinguishes itself by incorporating blockchain-based governance to enforce immutable operational rules and p

mujoco_menagerie is a curated library of physical robot specifications and XML model definitions designed for standardized dynamics and contact simulation. It provides a collection of high-quality robot model files for humanoids, quadrupeds, and manipulators, alongside detailed kinematic and inertial parameters used to reproduce real-world robot behavior in virtual environments. The project serves as a repository of robotics simulation assets and MJCF model definitions optimized for accuracy. It includes standardized model libraries specifically for bipedal, quadrupedal, and humanoid hardware

This project is a comprehensive programming course and educational curriculum designed to transition developers from basic scripting to advanced software development. It provides structured guides and technical exercises focusing on language internals, professional software architecture, and sophisticated programming techniques. The curriculum distinguishes itself through a deep focus on language internals, analyzing object behavior and memory efficiency to improve execution speed. It provides specialized instruction on metaprogramming using decorators and dynamic attributes, as well as async

Spinning Up is a deep reinforcement learning curriculum designed to teach the theory and implementation of deep reinforcement learning algorithms. It serves as a guided educational resource for understanding how agents interact with environments through mathematical models and code. The project provides a research roadmap consisting of a curated collection of influential research papers and theoretical concepts. This literature study is designed to guide a deeper exploration of specific reinforcement learning domains. The curriculum covers the implementation of reinforcement learning logic t

TensorFlow-Tutorials is a collection of educational resources and guided tutorials for implementing machine learning models using the TensorFlow framework. It provides instructional material and videos for building deep learning architectures across diverse domains, including computer vision, natural language processing, and time-series prediction. The project offers practical guides for developing specific applications such as image captioning, style transfer, and machine translation. It emphasizes a structured approach to learning, ranging from simple linear models to complex reinforcement

Python-tutorial is a comprehensive programming resource and offline documentation generator designed to take learners from absolute beginners to advanced practitioners. The curriculum is structured through hierarchical concept progression, separating materials into distinct modular documents that cover fundamental syntax alongside complex language patterns for scaling software projects. The resource includes integrated solution verification, pairing practice exercises with complete reference implementations so learners can verify their work independently. Additionally, the project features a

Cartographer is a cross-platform robotics library and framework for simultaneous localization and mapping in 2D and 3D spaces. It functions as a real-time mapping engine that constructs environmental maps while tracking a device's position and orientation using continuous sensor data processing. The system implements real-time SLAM to generate precise maps for autonomous navigation. It utilizes a localization system that determines a device's state within a mapped environment across different hardware platforms and sensor configurations. The framework covers spatial estimation through non-li

This project is a computational linguistics course and academic repository providing a structured curriculum for learning natural language processing. It consists of educational materials, lecture slides, and a programming curriculum designed to teach the intersection of linguistic fundamentals and statistical data-driven processing. The repository integrates theoretical study with practical application through a combination of university-level lecture modules, curated textbook materials, and a collection of academic research papers. It emphasizes hands-on learning via linguistic data annotat