Pros of grid_map

• Specialized for 2D grid maps, offering efficient operations for terrain representation
• Provides a rich set of tools for map manipulation, including iterators and filters
• Supports multi-layered maps for storing various data types per cell

Cons of grid_map

• Limited to 2D representations, lacking the volumetric capabilities of octomap
• May require more memory for large maps compared to octomap's octree structure
• Less suitable for 3D environment modeling and obstacle avoidance in robotics

Code Comparison

grid_map example:

grid_map::GridMap map({"elevation", "normal_x", "normal_y", "normal_z"});
map.setGeometry(grid_map::Length(1.0, 1.0), 0.01);
map["elevation"].setConstant(0.0);

octomap example:

octomap::OcTree tree(0.1);
octomap::point3d sensor_origin(0.0, 0.0, 0.0);
tree.insertPointCloud(point_cloud, sensor_origin);
tree.updateInnerOccupancy();

The grid_map code focuses on creating a 2D grid with multiple layers, while the octomap code demonstrates creating a 3D octree and inserting point cloud data.

Pros of elevation_mapping

• Specialized for 2.5D terrain mapping, ideal for ground robots
• Supports multi-layered maps for richer terrain representation
• Efficient updates and fusion of new sensor data

Cons of elevation_mapping

• Limited to 2.5D representations, not suitable for full 3D environments
• May require more memory for storing multiple layers
• Less widespread adoption compared to octomap

Code Comparison

elevation_mapping:

grid_map::GridMap map;
map.add("elevation", Matrix::Zero(rows, cols));
map.add("variance", Matrix::Constant(rows, cols, 0.01));

octomap:

octomap::OcTree tree(resolution);
tree.updateNode(point3d(1.0, 2.0, 3.0), true);
tree.updateInnerOccupancy();

Key Differences

• elevation_mapping focuses on terrain representation, while octomap is a general-purpose 3D mapping solution
• octomap uses probabilistic occupancy grids, whereas elevation_mapping uses elevation and variance layers
• elevation_mapping is better suited for ground robots, while octomap is more versatile for various robotic applications

Both libraries have their strengths and are valuable tools in robotics and mapping, with the choice depending on the specific application requirements and constraints.

Pros of VINS-Fusion

• Provides a complete visual-inertial state estimation system for various applications
• Supports multiple sensors and fusion strategies
• Offers real-time performance and high accuracy

Cons of VINS-Fusion

• More complex setup and configuration compared to Octomap
• Requires specific sensor inputs (visual and inertial data)
• May have higher computational requirements

Code Comparison

VINS-Fusion (C++):

void System::ProcessIMU(double t, const Vector3d &linear_acceleration, const Vector3d &angular_velocity)
{
    if (!initial_alignment_flag)
        return;
    estimator.processIMU(t, linear_acceleration, angular_velocity);
}

Octomap (C++):

void OcTree::insertPointCloud(const Pointcloud& scan, const octomap::point3d& sensor_origin,
                              double maxrange, bool lazy_eval) {
  KeySet free_cells, occupied_cells;
  if (this->computeUpdate(scan, sensor_origin, free_cells, occupied_cells, maxrange)) {
    // insert data into tree  
    for (KeySet::iterator it = free_cells.begin(); it != free_cells.end(); ++it) {
      updateNode(*it, false, lazy_eval);
    }
    for (KeySet::iterator it = occupied_cells.begin(); it != occupied_cells.end(); ++it) {
      updateNode(*it, true, lazy_eval);
    }
  }
}

Pros of navigation2

• Comprehensive navigation stack for ROS 2, including planning, control, and recovery behaviors
• Modular architecture allowing easy customization and extension of navigation capabilities
• Active development and community support within the ROS ecosystem

Cons of navigation2

• Steeper learning curve due to its complexity and integration with ROS 2
• Potentially higher computational requirements for full navigation stack

Code Comparison

octomap:

#include <octomap/octomap.h>

octomap::OcTree tree(0.1);  // Create an octree with resolution 0.1
tree.updateNode(point3d(1.0, 2.0, 3.0), true);  // Insert a point

navigation2:

#include "nav2_core/behavior_tree_navigator.hpp"

auto node = std::make_shared<rclcpp::Node>("nav2_node");
nav2_core::BehaviorTreeNavigator navigator(node);
navigator.followPath(path);  // Navigate along a given path

Summary

While octomap focuses on efficient 3D mapping and spatial representation, navigation2 provides a complete navigation solution for ROS 2-based robots. octomap excels in memory-efficient 3D environment modeling, while navigation2 offers a broader set of tools for robot navigation, including path planning and obstacle avoidance. The choice between them depends on the specific requirements of your robotics project and whether you need a full navigation stack or just efficient 3D mapping capabilities.