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

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.

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

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

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

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.

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).

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

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.

Implemented simultaneous localization and mapping (SLAM) using odometry, 2-D LiDAR scans, and stereo camera measurements from an autonomous car. And the map is displayed using the 2d Occupancy grid.

ROS 기반 Lidar SLAM: Occupancy Grid Map 생성 및 TF 브로드캐스트 포함

ECE 276A Particle Filter Slam on Occupancy Grid Map and Texture Mapping

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

Perform graph SLAM and create a 2D occupancy grid and 3D Map from a simulated environment using robot with the RTAB-Map package.

Using SLAM and RTAP-Map ROS packages to create create a 2D occupancy grid and 3D octomap from a provided simulated environment.

Multi-agent SLAM system with particle filters, occupancy grid mapping, and RANSAC-based map merging for concurrent exploration of unknown 2D environments.

Implemented SLAM_Gmapping to create a 2-D occupancy grid map of the robot's surroundings from laser and pose data collected by the differential drive robot.

Multi-robot Slam - Academic project for Learning in Robotics (ESE 650) course at UPenn. 2D Slam using Lidar readings using occupancy grid mapping. Finally multiplle robots individual maps are merged with loop closing, scan alignment, etc.

Slam - Academic project for Learning in Robotics (ESE 650) course at UPenn. 2D Slam using Lidar readings using occupancy grid mapping. Finally the camera feed was overlayed on the created map to get the texture of the 2D environment.

This repository contains the 2D SLAM of a race track , generating occupancy grid of static map of the race track, Path planning for optimum speed lap finishes on AutoDrive Simulator Environment.

This repository demonstrates how to generate a 2D occupancy grid map using ORB-SLAM3 in a ROS 2 (Humble) environment. It leverages visual SLAM to build an accurate map of the environment in real time, converting the 3D pose and point cloud data into a usable 2D grid for navigation and planning.

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.

artgslam is a SLAM backend designed to handle sensor data acquisition and sonar signal processing for Adept mobile robots, particularly the Amigobot platform. It processes odometry and sonar data to build and publish occupancy grid maps and robot pose information within a ROS framework.