Grid Map vs OctoMap: which one for 3D mapping?

Grid Map is for 2.5D grid mapping with multiple layers, ideal for terrain elevation and surface data. OctoMap is better for full 3D volumetric mapping with occupancy. Use Grid Map for navigation on rough terrain and OctoMap for complete 3D environment modeling.

How to install Grid Map in ROS Noetic?

Install via apt with `sudo apt-get install ros-noetic-grid-map`, or build from source by cloning the repository into your catkin workspace and compiling with catkin_make in Release mode for best performance, as detailed in the Installation section.

Grid Map tutorial for creating an elevation map

Start with the simple_demo and tutorial_demo nodes in the grid_map_demos package, which show map creation, data addition, and publishing. The linked book chapter provides a comprehensive introduction and tutorial.

How to apply OpenCV filters to a grid map layer?

Use the grid_map_cv package to convert grid maps to OpenCV images, apply OpenCV functions, and convert back. The opencv_demo demonstrates this with examples like image processing and resolution changes.

Does Grid Map support large maps without performance issues?

The circular buffer helps with efficient repositioning, but large maps with many layers can increase memory usage. Optimize by using Eigen operations directly and efficient iterators, as benchmarked in the iterator_benchmark node.

Can I use Grid Map without ROS?

Yes, but only the grid_map_core package has no ROS dependencies and can be used standalone. Key features like ROS interfaces, visualization plugins, and filters require ROS, limiting its utility outside that ecosystem.

grid_map — C++ 2D Grid Maps for Robotics

What is grid_map?

Grid Map is a C++ library with ROS interface for managing two-dimensional grid maps with multiple data layers in mobile robotics. It is designed to store and process various types of environmental data, such as elevation, variance, and traversability, particularly for rough terrain navigation. The library provides efficient data structures, seamless integration with robotics tools, and powerful filtering capabilities for real-time mapping applications.

Target Audience

Robotics researchers and engineers working on mobile robotic mapping, especially those focused on legged robots, autonomous navigation, and terrain analysis. It is also suitable for developers building perception and mapping pipelines within the ROS ecosystem.

Value Proposition

Developers choose Grid Map for its efficient circular buffer implementation, which allows non-destructive map repositioning, and its deep integration with Eigen for high-performance data manipulation. Its comprehensive ROS and OpenCV interfaces, along with a flexible filter pipeline, make it a versatile and powerful tool for complex robotic mapping tasks.

Universal grid map library for mobile robotic mapping

Use Cases

Best For

Building elevation maps for legged robot navigation on rough terrain
Processing multi-layered sensor data (e.g., LIDAR, cameras) in robotic mapping
Real-time map manipulation and visualization in ROS-based systems
Converting between grid maps and other data formats like point clouds or occupancy grids
Applying image processing filters to grid map layers using OpenCV
Developing custom mapping algorithms with efficient iterator-based access patterns

Not Ideal For

Projects outside the ROS ecosystem requiring a standalone, framework-agnostic mapping library
Simple 2D occupancy mapping where basic ROS costmap_2d or a plain grid suffices
Teams needing production-stable code with long-term support and no breaking changes
Applications demanding full 3D volumetric mapping (e.g., dense SLAM) instead of 2.5D grid layers

Pros & Cons

Pros

Efficient Multi-Layered Storage

Supports any number of data layers for 2.5D mapping, allowing diverse data like elevation, color, and traversability in one structure, as highlighted in the overview.

Non-Destructive Map Repositioning

Uses a circular buffer to shift map positions without copying data, crucial for real-time mobile robotics, demonstrated in the move() method animations.

Deep Eigen Integration

Stores data as Eigen types, enabling direct use of Eigen's linear algebra algorithms for efficient manipulation, with examples like layer arithmetic in the iterators section.

Comprehensive ROS Interface

Provides direct conversion to and from ROS messages like PointCloud2 and OccupancyGrid, with packages for costmap_2d, PCL, and OctoMap integration.

Powerful Filter Pipeline

Includes a range of filters and mathematical expression evaluation via EigenLab, shown in the filters_demo for tasks like normal vector computation and inpainting.

Cons

Research Code Stability

The README disclaims fitness for purpose and notes it changes often, making it risky for production systems that require reliability and long-term support.

Limited PointCloud2 Conversion

PointCloud2 can only be converted one-way due to an acknowledged issue (#158), restricting bidirectional data flow with common ROS point cloud formats.

ROS-Centric Complexity

Heavily dependent on ROS for core features like visualization and interfaces, adding setup overhead and limiting usability for non-ROS or lightweight projects.

What is grid_map?

Target Audience

Value Proposition

Use Cases

Best For

Building elevation maps for legged robot navigation on rough terrain
Processing multi-layered sensor data (e.g., LIDAR, cameras) in robotic mapping
Real-time map manipulation and visualization in ROS-based systems
Converting between grid maps and other data formats like point clouds or occupancy grids
Applying image processing filters to grid map layers using OpenCV
Developing custom mapping algorithms with efficient iterator-based access patterns

Not Ideal For

Projects outside the ROS ecosystem requiring a standalone, framework-agnostic mapping library
Simple 2D occupancy mapping where basic ROS costmap_2d or a plain grid suffices
Teams needing production-stable code with long-term support and no breaking changes
Applications demanding full 3D volumetric mapping (e.g., dense SLAM) instead of 2.5D grid layers

Pros & Cons

Pros

Efficient Multi-Layered Storage

Supports any number of data layers for 2.5D mapping, allowing diverse data like elevation, color, and traversability in one structure, as highlighted in the overview.

Non-Destructive Map Repositioning

Uses a circular buffer to shift map positions without copying data, crucial for real-time mobile robotics, demonstrated in the move() method animations.

Deep Eigen Integration

Stores data as Eigen types, enabling direct use of Eigen's linear algebra algorithms for efficient manipulation, with examples like layer arithmetic in the iterators section.

Comprehensive ROS Interface

Provides direct conversion to and from ROS messages like PointCloud2 and OccupancyGrid, with packages for costmap_2d, PCL, and OctoMap integration.

Powerful Filter Pipeline

Includes a range of filters and mathematical expression evaluation via EigenLab, shown in the filters_demo for tasks like normal vector computation and inpainting.

Cons

Research Code Stability

The README disclaims fitness for purpose and notes it changes often, making it risky for production systems that require reliability and long-term support.

Limited PointCloud2 Conversion

PointCloud2 can only be converted one-way due to an acknowledged issue (#158), restricting bidirectional data flow with common ROS point cloud formats.

ROS-Centric Complexity

Heavily dependent on ROS for core features like visualization and interfaces, adding setup overhead and limiting usability for non-ROS or lightweight projects.

grid_map

What is grid_map?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

grid_map

What is grid_map?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?