Bonxai is a library that implements a compact hierarchical data structure that can store and manipulate volumetric data, discretized on a three-dimensional grid (AKA, a "Voxel Grid").
Bonxai data structure is:
- Sparse: it uses only a fraction of the memory that a dense 3D voxel grid would use.
- Unbounded: you don't need to define the boundary of the 3D space (*).
(*) The dimension of the 3D space is virtually "infinite": since 32-bits indices are used, given a voxel size of 1 cm, the maximum range of the X, Y and Z coordinates would be about 40.000 Km. As a reference the diameter of planet Earth is 12.000 Km.
If you are familiar with Octomap and Octrees, you know that those data structures are also sparse and unbounded.
On the other hand, Bonxai is much faster and, in some cases, even more memory-efficient than an Octree.
This work is strongly influenced by OpenVDB and it can be considered an implementation of the original paper, with a couple of non-trivial changes:
K. Museth,
“VDB: High-Resolution Sparse Volumes with Dynamic Topology”,
ACM Transactions on Graphics 32(3), 2013. Presented at SIGGRAPH 2013.
You can read the previous paper here.
There is also some overlap with this other paper, but their implementation is much** simpler, even if conceptually similar:
Eurico Pedrosa, Artur Pereira, Nuno Lau
"A Sparse-Dense Approach for Efficient Grid Mapping"
2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Bonxai is currently under development and I am building this mostly for fun and for educational purposes. Don't expect any API stability for the time being.
Take these numbers with a grain of salt, since they are preliminary and the benchmark is strongly influenced by the way the data is stored. Anyway, they gave you a fair idea of what you may expect, in terms of performance.
-------------------------------------------
Benchmark Time
-------------------------------------------
Bonxai_Create 1165 us
Octomap_Create 25522 us
Bonxai_Update 851 us
Octomap_Update 3824 us
Bonxai_IterateAllCells 124 us
Octomap_IterateAllCells 698 us
- Create refers to creating a new VoxelGrid from scratch
- Update means modifying the value of an already allocated VoxelGrid.
- IterateAllCells will get the value and the coordinates of all the existing cells.
The core of Bonxai is a header-only library that you can simply copy into your project and include like this:
#include "bonxai/bonxai.hpp"To create a VoxelGrid, where each cell contains an integer value and has size 0.05.
double voxel_resolution = 0.05;
Bonxai::VoxelGrid<int> grid( voxel_resolution );Nothing prevents you from having more complex cell values, for instance:
Bonxai::VoxelGrid<Eigen::Vector4d> vector_grid( voxel_resolution );
// or
struct Foo {
int a;
double b;
};
Bonxai::VoxelGrid<Foo> foo_grid( voxel_resolution );To insert values into a cell with coordinates x, y and z, use a
VoxelGrid::Accessor object.
In the next code sample, we will create a dense cube of cells with value 42:
// Each cell will contain a `float` and it will have size 0.05
double voxel_resolution = 0.05;
Bonxai::VoxelGrid<float> grid( voxel_resolution );
// Create this accessor once, and reuse it as much as possible.
auto accessor = grid.createAccessor();
// Create cells with value 42.0 in a 1x1x1 cube.
// Given voxel_resolution = 0.05, this will be equivalent
// to 20x20x20 cells in the grid.
for( double x = 0; x < 1.0; x += voxel_resolution ) {
for( double y = 0; y < 1.0; y += voxel_resolution ) {
for( double z = 0; z < 1.0; z += voxel_resolution ) {
// discretize the position {x,y,z}
Bonxai::CoordT coord = grid.posToCoord(x, y, z);
accessor.setValue( coord, 42.0 );
}
}
}
// You can read (or update) the value of a cell as shown below.
// If the cell doesn't exist, `value_ptr` will be `nullptr`,
Bonxai::CoordT coord = grid.posToCoord(x, y, z);
float* value_ptr = accessor.value( coord );Bonxai::VoxelGrid is not thread-safe, for write operations.
If you want to access the grid in read-only mode, you can
use multi-threading, but each thread should have its own
accessor.
- serialization to/from file.
- full implementation of the Octomap algorithm (ray casting + probability map).
- integration with ROS 2.
- RViz2 visualization messages.
- integration with FCL for collision detection (?)
What is the point of reimplementing OpenVDB?
- The number one reason is to have fun and to learn something new :)
- I want this library to be small and easy to integrate into larger projects. The core data structure is less than 1000 lines of code.
- It is not an "exact" rewrite, I modified a few important aspects of the algorithm to make it slightly faster, at least for my specific use cases.
How much memory does it use, compared with Octomap?
It is... complicated.
If you need to store very sparse point clouds, you should expect Bonxai to use more memory (20-40% more). If the point cloud is relatively dense, Bonxai might use less memory than Octomap (less than half).
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