DB-TSDF: Directional Bitmask-based Truncated Signed Distance Fields for Efficient Volumetric Mapping

Accepted at the IEEE International Conference on Robotics and Automation (ICRA) 2026

DB-TSDF presents a high-efficiency, CPU-only framework for volumetric mapping. It utilizes a novel directional bitmask-based integration scheme to incrementally fuse LiDAR data into a dense voxel grid.

Key features include:

• Directional Kernels: Efficiently model beam geometry and occlusion in 3D.
• Bitmask Encoding: Ensures constant-time updates per scan, independent of grid resolution.
• High Performance: Multi-threaded C++ implementation fully integrated with ROS 2.

The design prioritizes predictable runtime and high-resolution reconstruction, making it an ideal solution for robotic platforms with limited GPU resources.

1. Prerequisites

Before you begin, make sure you have ROS 2 Humble and Ubuntu 22.04 (or higher) installed on your system. These are the core requirements for the project to run smoothly. If you haven't installed ROS 2 Humble yet, follow the official installation guide for your platform. This guide will walk you through all the necessary steps to set up the core ROS 2 environment on your system.

2. Installation

Install localy

To install and build the project, simply clone the repository as follows:

git clone https://github.com/robotics-upo/DB-TSDF.git
cd ..
colcon build
source install/setup.bash

Install using Docker

Follow these steps to build and run DB-TSDF inside a Docker container:

1. Clone the repository:

   git clone https://github.com/robotics-upo/DB-TSDF.git
   cd DB-TSDF

2. Build the Docker image:

   docker build -t db_tsdf_ros2:humble .

3. Allow Docker to access the X server (for GUI like RViz):

   xhost +local:docker

4. Run the container

   docker run -it \
     --env="DISPLAY" \
     --env="QT_X11_NO_MITSHM=1" \
     --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
     --name db_tsdf_container \
     db_tsdf_ros2:humble

The Dockerfile sets up the entire environment and downloads the DB-TSDF code automatically.

3. Running the Code

The launch system uses a single main launch file (db_tsdf_launch.py) and a config argument to specify which dataset configuration to load.

This argument (e.g., college) will automatically load the corresponding parameter file (college.yaml) and the RViz configuration (college.rviz).

First, open a terminal, source your workspace, and run the launch file. The node will start, RViz will open, and the system will wait for data.

To launch using the college configuration:

ros2 launch db_tsdf db_tsdf_launch.py config:=college

To feed data, simply play a recorded ROS 2 bag in another terminal:

ros2 bag play /path/to/your_dataset

4. Configuration

The system is highly configurable via YAML parameters (e.g., config/college.yaml).

Core Parameters

Parameter	Type	Description	Default
in_cloud	string	Input PointCloud2 topic	/os_cloud_node/points
odom_frame_id	string	Fixed frame for TF lookup	odom
use_tf	bool	Enable/Disable TF transformations	True

Grid Definition

Parameter	Type	Description	Default
tdf_grid_res	float	Voxel side length in meters	0.05
tdf_max_cells	int	Max active cells in hash table	75000
tdfGridSize*_low/high	float	Physical volume boundaries	+/-100

Integration Kernel

Parameter	Type	Description	Default
kernel_size	int	Kernel size (odd number)	11
bins_az / bins_el	int	Angular discretization	360
occ_min_hits	int	Min measurements to mark occupied	50

5. Output Data and Services

The node provides ROS 2 services to export the reconstructed map:

• Mesh (STL)

  ros2 service call /save_grid_mesh std_srvs/srv/Trigger "{}"

• Voxel Point Cloud

  ros2 service call /save_grid_ply std_srvs/srv/Trigger "{}"   # grid_data.ply
  ros2 service call /save_grid_pcd std_srvs/srv/Trigger "{}"   # grid_data.pcd

• Voxel Statistics (CSV)

  ros2 service call /save_grid_csv std_srvs/srv/Trigger "{}"

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## Citation If you use DB-TSDF in your research, please cite our ICRA 2026 paper:

@inproceedings{maese2026dbtsdf,
  title={DB-TSDF: Directional Bitmask-based Truncated Signed Distance Fields for Efficient Volumetric Mapping},
  author={Maese, Jose E. and Caballero, Fernando and Merino, Luis},
  booktitle={2026 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2026}
}

Acknowledgements

This work was supported by the grants PICRA 4.0 (PLEC2023-010353), funded by the Spanish Ministry of Science and Innovation and the Spanish Research Agency (MCIN/AEI/10.13039/501100011033); and INSERTION (PID2021-127648OB-C31), funded by the "Agencia Estatal de Investigación - Ministerio de Ciencia, Innovación y Universidades" and the "European Union NextGenerationEU/PRTR".