Indoor/Outdoor SLAM using Stereo Vision. This project used ORB_SLAM2 with ZED stereo camera to achieve the task of SLAM and has a custom 2D occupancy grid mapping algorithm using depth.

SLAM with occupancy grid and particle filter, using lidar, joints, IMU and odometry data from THOR humanoid robot

KAZE Image feature-based (keypoint) occupancy grid map-merging approach for collaborative SLAM.

A modular SLAM system combining Particle Filter-based localization, Occupancy Grid Mapping (OGM), Dynamic Window Approach (DWA) for real-time obstacle avoidance, and D* Lite for global path replanning. This project integrates both probabilistic mapping and real-time motion planning, suitable for research and educational use in robotics.

A FastSLAM algorithm for Occupancy Grid Maps based on Rao-Blackwellized Particle Filters

This is a SLAM (Simultaneous Localization And Mapping) project. It is going to use odometry, inertial, 2-D laser range, and RGBD measurements to localize a differential-drive robot and build a 2D occupancy grid map. Furthermore, floor texture would be given to the 2D map by utilizing the measurement obtained from RGBD cameras on robot.

No description available

Simulator for FastSLAM with Occupancy Grid Maps. Currently being updated.

Create an occupancy grid map using lidar point cloud data for a generated driving scenario.

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ESE 650: Learning in Robotics, Project 4, SLAM using Particle Filter and Occupancy Grid Mapping with 3D Map Generation

Implentation of Occupancy Grid based Simultaneous Localization and Mapping (SLAM) using the Hector quadrotor package in ROS. All scripts were written in Python.

No description available

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SLAM, Path Planning, Localization, ICP, Occupancy Grid Maps, Other Stuff...

SLAM on turtlebot using kinect to generate 3D Occupancy Grids (Octomap)

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Integrate a Graph based SLAM and Occupancy Grid Mapping algorithm.

Implementation of the Fast-SLAM algorithm on a Turtlebot3 robot using ArUco markers as landmarks within a ROS framework. We created a micro-simulation environment, developed an ArUco detection algorithm, and optimized key parameters. We also integrated an Occupancy Grid Mapping algorithm and RViz visualization.

Developed a LiDAR-based SLAM system combining odometry, ICP scan matching, and pose graph optimization. Achieved drift reduction and global map consistency through loop closure constraints with GTSAM. Generated a high-resolution occupancy grid and textured floor map using multi-sensor fusion (LiDAR and RGB-D).

RTAB-Map SLAM in ROS Noetic with Custom Robot for self localization and mapping implementation. The project leverages rtabmap_ros for generating 2D occupancy grids and 3D octomaps, controlled via the teleop ROS node. The robot explores and maps the environment using RTAB-Map for real-time, appearance-based mapping.

Implementation of occupancy grid maping, SLAM, A star path planning and exploration.

Indoor/Outdoor SLAM using Stereo Vision. This project used ORB_SLAM2 with ZED stereo camera to achieve the task of SLAM and has a custom 2D occupancy grid mapping algorithm using depth.

SLAM with localization using the Particle Filter and Mapping using the Occupancy Grid Mapping.

Research Code for "Generalizing corrective gradient refinement in RBPF for occupancy grid LIDAR SLAM" ROBIO 2017

SLAM run on Raspberry Pi: Occupancy grid mapping, Particle Filter localization and A* path planning & exploration

Autonomous mapping and navigation in ROS 2 for Crazyflie drones using the Multiranger deck. Includes Particle Filter SLAM, occupancy grid mapping, and wall-following for real and simulated environments.

Implemented simultaneous localization and mapping (SLAM) using odometry, 2-D LiDAR scans, and stereo camera measurements from an autonomous car to localize the robot and build a 2-D occupancy grid map of the environment.

Developed a full ROS1 pipeline for 2D mapping and navigation, using laser scan fusion and the gmapping SLAM algorithm to generate an occupancy grid map, followed by Stage simulation and a tuned navigation stack (DWA, AMCL) for localization and autonomous collision-free path planning.

Lets you build, save, and load rooms with permanent (wall) and temporary (box) blocks. Generates an occupancy grid. Lets you run a simulation, and the controls and measurements from the laser distance sensors are saved at each time step. Perfect for generating data to use for testing SLAM, Localization, or Mapping algorithms. Written in GML with the GameMaker Studio game engine.