DynoSAM: Dynamic Object Smoothing and Mapping

A Stereo/RGB-D Visual Odometry pipeline for Dynamic SLAM.

DynoSAM estimates camera poses, object motions/poses, as well as static background and temporal dynamic object maps. It provides full-batch, sliding-window, and incremental optimization procedures and is fully integrated with ROS2.

📚 Publications

The offical code used for our paper:

• Jesse Morris, Yiduo Wang, Mikolaj Kliniewski, Viorela Ila, DynoSAM: Open-Source Smoothing and Mapping Framework for Dynamic SLAM. Accepted Transactions on Robotics (T-RO) Visual SLAM Special Issue, 2025.

** Update December 2025 **

Our work has been accepted to IEEE Robotics and Automation Letters (RA-L)

** Update November 2025 **

Our work has been accepted to IEEE Transactions on Robotics (T-RO)

** Update September 2025 **

This code now also contains the code for our new work

• J.Morris, Y. Wang, V. Ila. Online Dynamic SLAM with Incremental Smoothing and Mapping, Accepted Robotics and Automation Letters (RA-L), 2025

We kindly ask to cite our papers if you find these works useful:

@misc{morris2025dynosam,
  author={Morris, Jesse and Wang, Yiduo and Kliniewski, Mikolaj and Ila, Viorela},
  journal={IEEE Transactions on Robotics},
  title={DynoSAM: Open-Source Smoothing and Mapping Framework for Dynamic SLAM},
  year={2025},
  doi={10.1109/TRO.2025.3641813}
}

@inproceedings{morris2025online,
  author={Morris, Jesse and Wang, Yiduo and Ila, Viorela},
  journal={IEEE Robotics and Automation Letters},
  title={Online Dynamic SLAM with Incremental Smoothing and Mapping},
  year={2026},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/LRA.2026.3655286}}
}

Related Publications

DynoSAM was build as a culmination of several works:

• J. Morris, Y. Wang, V. Ila. The Importance of Coordinate Frames in Dynamic SLAM. IEEE Intl. Conf. on Robotics and Automation (ICRA), 2024
• J. Zhang, M. Henein, R. Mahony, V. Ila VDO-SLAM: A Visual Dynamic Object-aware SLAM System, ArXiv
• M. Henein, J. Zhang, R. Mahony, V. Ila. Dynamic SLAM: The Need for Speed.2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020.

📖 Overview

Key Features (Nov 2025 Update)

• 🚀 CUDA Integration: Front-end acceleration.
• 🧠 TensorRT: Integrated object detection and tracking.
• ⚡ Sparse Tracking: Options to move away from dense optical flow.
• 📦 Modular: System broken into packages for faster compile times.
• 🐳 Docker: Updated image with new dependencies.

Documentation

We auto generate Doxygen code docs for all classes in DynoSAM. The code docs are up-to-date with the main branch.

1. ⚙️ Installation

We provide a detailed installation guide including dependencies and Docker support. See detailed instructions here: Insallation instructions

1.1 Edge Device Support

We currently support building for AARM64 devices and DynoSAM has been tested on an NVIDIA ORIN NX. Docker file support and more details are provided in the install instructions.

NOTE: DynoSAM does not currently run real-time on the ORIN NX (mostly bottlenecked by the object detection process). On a more powerful device better performance is expected.

Also see the Docker README.md and the dynosam_nn README.md for more information on hardware and performance.

2. 🚀 Running DynoSAM

2.1 Paramters

DynoSAM is configured using a combination of YAML files (pipeline, frontend, datasets) and GFLAGS (overridable command-line parameters). ROS parameters are used only for input file paths.

All .yaml and .flags files must be placed in a single parameter folder which defines the params_path.

params/
  FrontendParams.yaml
  PipelineParams.yaml
  [DatasetParams.yaml]
  [CameraParams.yaml]
  *.flags

• YAML files are loaded using config_utilities.
• GFlags provide run-time reconfiguration (important for automated experiments).
• ROS parameters are used sparingly (mainly for file paths).

NOTE: Note: Additional GFlags cannot be passed through ros2 launch. To override GFlags pass them directly with ros2 run, or modify the flag files inside the params folder.

To print active parameters:

ros2 run dynosam_utils eval_launch.py --show_dyno_args=true

To see all gflag options run:

ros2 run dynosam_ros dynosam_node --help

2.2 Quick Start (Launch File)

Launch the full pipeline with ROS2:

ros2 launch dynosam_ros dyno_sam_launch.py \
    params_path:=<path-to-params-folder> \
    dataset_path:=<path-to-dataset> \
    v:=<verbosity> \
    --data_provider_type=<data_set_loader_type> \
    --output_path=</path/to/output/folder>

The launch file:

• Loads all .flags files in the parameter folder,
• Applies dataset provider selection via --data_provider_type,
• Logs outputs to --output_path (must exist beforehand).

2.3 Experiment & Evaluation Launcher

For fine‑grained control and automated experiments, use:

ros2 run dynosam_utils eval_launch.py \
    --dataset_path <path> \
    --params_path <absolute path> \
    --output_path <path> \
    --name <experiment name> \
    --run_pipeline \
    --run_analysis \
    <extra GFLAG cli arguments>

This script:

• Automates running the pipeline and evaluations,
• Forwards all extra CLI arguments to DynoSAM (allowing any GFLAG override),
• Creates result folders output_path/name/ automatically.

Example:

ros2 run dynosam_utils eval_launch.py \
    --output_path=/tmp/results \
    --name=test \
    --run_pipeline \
    --data_provider_type=2

2.4 Programmatic Execution (Python)

All command‑line behaviour can be replicated in Python. See: run_experiments_tro.py for examples.

3. 📂 Datasets

3.1 Pre-processing Image data

DynoSAM requires input image data in the form:

• RGB
• Depth/Stereo
• Dense Optical Flow
• Dense Semantic Instance mask

Each image is expected in the following form:

• rgb image is expected to be a valid 8bit image (1, 3 and 4 channel images are accepted).
• depth must be a CV_64F image where the value of each pixel represents the metric depth.
• mask must be a CV_32SC1 where the static background is of value $0$ and all other objects are lablled with a tracking label $j$. $j$ is assumed to be globally consistent and is used to map the tracked object $j$ to the ground truth.
• flow must be a CV_32FC2 representing a standard optical-flow image representation.

For dense optical flow (ie. pre Novemeber 2025) we use RAFT. Currently this pre-processing code is not available.

For instance segmentation we use YOLOv8 for both image pre-processing and online processing. Both Python and C++ (using TensorRT acceleration) models can be found in the dynosam_nn package. See the REAMDE for more details.

• If prefer_provided_optical_flow: true (YAML), the pipeline expects a dense flow image. Otherwise, it falls back to sparse KLT.
• If prefer_provided_object_detection: true (YAML) , an instance mask must be provided. If false, masks are generated online via.

3.2 Running with pre-processed data

DynoSAM provides dataset loaders that parse pre-processed images (ie. depth, optical flow, masks), and ground truth into a unified format.

All official datasets are hosted at the ACFR-RPG Datasets page. To download a dataset, create a directory for the relevant dataset and, within the directory, run the command

wget -m -np -nH --cut-dirs=4 -R "index.html*" https://data.acfr.usyd.edu.au/rpg/dynosam/[Dataset]/[Subset]

For example, for the kitti dataset with subset 0004, create and enter the directory kitti-0004 download all files:

wget -m -np -nH --cut-dirs=4 -R "index.html*" https://data.acfr.usyd.edu.au/rpg/dynosam/kitti/0004

NOTE: when developing using docker, download the sequences into the data folder mounted into the docker container so it may be accessed by the program.

The following datasets are officially supported:

Dataset	Dataset ID	Notes
KITTI Tracking	0	Uses modified version with GT motion, flow, masks.
Virtual KITTI 2	1	Raw dataset supported directly.
Cluster-SLAM (CARLA)	2	Raw dataset available; we recommend our processed version.
Oxford Multimotion (OMD)	3	Modified 2024 version available.
TartanAir Shibuya	5	Preprocessed version supported.
VIODE	6	IMU-enabled sequences supported.

To run a specific dataset only two GFLAGS are required:

--dataset_path
--data_provider_type

where --dataset_path points to the location of the dataset and --data_provider_type should be set to Dataset ID.

NOTE: when using ground truth for evaluation it is important to ensure that prefer_provided_object_detection: false so that the pre-processing object masks are used. This ensures that tracking label $j$ used within the pipeline aligns with the ground truth label.

3.3 Online Data

DynoSAM can also run from data provided by ROS.

To run with online data (e.g. from raw sensor data) set the ROS parameter:

online:=True

DynoSAM can run in two input modes which can be set using the ROS parameter input_image_mode:

• ALL (input_image_mode=0)
• RGBD (input_image_mode=1).

When in ALL mode, all image-preprocessing is done upstream and five inputs are required: By default this node subscribes to five topics:

• dataprovider/image/camera_info (sensor_msgs.msg.CameraInfo)
• dataprovider/image/rgb (sensor_msgs.msg.Image)
• dataprovider/image/depth (sensor_msgs.msg.Image)
• dataprovider/image/mask (sensor_msgs.msg.Image)
• dataprovider/image/flow (sensor_msgs.msg.Image)

In RGBD mode, only camera intrinsics, rgb and depth images are required and all other image processing (including object detection) is done as part of the pipeline.

3.2.1 Running using RBG-D camera

To run from data provided by an RGB-D camera use

ros2 launch dynosam_ros dyno_sam_online_rgbd_launch.py

and remap the topics accordingly.

4. 🕹️ System Configuration

4.1 Backend Setup

Running Different Backends

DynoSAM supports multiple backend formulations. Set the backend_updater_enum flag to switch between them.

Enum	Formulation	Paper	Description
0	WCME	TRO 2025	World-Centric Motion Estimator
1	WCPE	TRO 2025	World-Centric Pose Estimator
2	HYBRID	RA-L 2025	Hybrid formulation
3	PARALLEL_HYBRID	RA-L 2025	Parallel Hybrid (Recommended for speed)

Many factor-graph related paramters exist in the backend.flags file.

4.2 Instance Segmentation

DynoSAM uses YOLOv8 for object detection. The model is integrated into the front-end and accelerated using CUDA and TensorRT.

To run the pipeline with online segmentation and object tracking set

prefer_provided_object_detection: true

See the dynosam_nn README.md for more detail on the models used and how to export the weights.

NOTE: the provided datasets contain pre-computed object masks with tracking labels $j$ that align with the ground truth. Use prefer_provided_object_detection: false.

4.3 IMU integration

We additionally support IMU integration using the PreintegrationFactor from GTSAM in the backend. However, this has only been tested on VIODE.

4.4 ROS Visualisation

All 3D visualisation in DynoSAM is done using RVIZ. Camera pose and point clouds are vizualised using standard ROS messages. Visualising the objects is more complex and we provide two different ways to do this which can be controlled at compile time using the cmake flag -DENABLE_DYNAMIC_SLAM_INTERFACES=ON/OFF

1. (ON, now default) Usage of the custom dynamic_slam_interfaces::msg::ObjectOdometry (in dynamic_slam_interfaces) to publish to current state of the each object per frame. Our custom RVIZ plugin rviz_dynamic_slam_plugins can be used to visualize this message type. The object id, current pose, velocity, path etc... will be shown for each object. See this README.md for more detail.
2. (OFF) Instead of using the custom messages, standard ROS visualisation messages are used instead to display the state of each object (object id, current pose...). This setup is therefore more complex, and results in many more advertised topics to achieve a similar (and less flexible) display than using the custom plugin/interface combination. As a result, this method of display is not preferred. See this README.md for more detail.

NOTE: the publishing of object states is meant primilarily for visualisation purposes and not for recording data used for evaluation. This is done using csv files in a different submodule: see Evaluation.

4.5 Tests

We use gtest for unit testing. This is installed automagically. When building with ROS, all tests will go into the install folder.

We provide a useful script to make running tests easier:

ros2 run dynosam_ros run_dynosam_gtest.py

Running unit tests for a specific package (ie. dynosam and dynosam_ros) can be specified using the --package argumement.

This script forwards all arguments to the test executable. This allows GFLAGS to be specified directly via the CLI.

run dynosam_ros run_dynosam_gtest.py --package=dynosam_ros --gtest_filter=TestConcepts*

5. 📊 Output & Metrics

The eval script should be used when generating results

ros2 run dynosam_utils eval_launch.py
  --output_path=/tmp/results \
  --name=test \
  --run_pipeline \
  --run_analysis

5.1 Reproducing Paper Results

Scripts for reproducing experiments:

• TRO 2025 experiments
• RAL 2025 experiments

While we attempt to maintain compatibility, minor differences may arise due to ongoing development.

5.2 Output Logs

When eval_launch.py is run with --run_analysis, DynoSAM generates a "results folder" containing:

• Logs: camera_pose_log.csv, object_motion_log.csv, etc.
• Plots: *results.pdf (ATE, RPE, Trajectory plots).
• Tables: result_tables.pdf (Summary statistics).

at the path /tmp/results/test.

The * specifies a module prefix (e.g. frontend, wcme) that is used to specify where the logged state-estimates have come from. An additional suffix can be added to the module name through the --updater_suffix flag.

6. 🧠 System Architecture

We follow a modular pipeline structure inspired by Kimera-VIO:

where a Pipeline/Module architecture connected by thread-safe queues

graph LR
    A[Input Data] --> B(Pipeline Wrapper)
    B --> C{ThreadSafe Queue}
    C --> D[Module Processing]
    D --> E{Output Queue}
    E --> F[Next Module]

is employed to connect data-provider, frontend, backend and visualizer modules.

Code-to-Theory Mapping

To help researchers connect the theory from our papers to the implementation, here is a mapping of our mathematical notation to the corresponding variable names and classes in the DynoSAM framework.

Mathematical Notation	Code Implementation	Description
$k$ (Time-step)	FrameId	Integer frame number (0...N). Used as the primary time index.
$i$ (Feature ID)	TrackletId	Unique feature tracking identifier.
$j$ (Object ID)	ObjectId	Unique tracked object label.
$X$ (Pose), $H$ (Motion)	gtsam::Pose3	Transformations for poses and motions, represented using the GTSAM library.
$\mathcal{O}_k$ (Observations)	Accessor	Interface class used by backend modules to extract and interact with internal factor graph data.

7. Contribution Guidelines

We strongly encourage you to submit issues, feedback and potential improvements. We follow the branch, open PR, review, and merge workflow.

Support

The developpers will be happy to assist you or to consider bug reports / feature requests. But questions that can be answered reading this document will be ignored. Please contact [email protected].

As of Nov 2024 I am still actively developing this project as part of my PhD at the ACFR. Things may change but I will do my best to ensure versions are tagged etc... as a result I will also be happy to address all bugs and feature requests! Send 'em through!!

To contribute to this repo, make sure you have pre-commit installed to enable checks.

pip install pre-commit

We provide a pre-commit configuration so just ensure you have the git hooks setup:

pre-commit install

We also provide a .clang-format file with the style rules that the repo uses, so that you can use clang-format to reformat your code.

8. BSD License

The DynoSAM framework is open-sourced under the BSD License, see LICENSE.