Offline Rao-Blackwellized Particle Filter SLAM Using Adaptive Sampling in ROS2

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Abstract

This repository explores an enhanced Rao-Blackwellized Particle Filter (RBPF) SLAM algorithm addressing particle resampling inefficiencies. By incorporating adaptive sampling techniques (dynamic weight normalization and multi-resolution sampling), this SLAM framework is implemented in ROS2 using C++ and tested on the TurtleBot3 platform.

Key contributions include:

• Reducing particle weight variance to minimize depletion.
• Efficient mapping with fewer particles.
• Improved computational overhead and mapping accuracy.

Key Highlights

• Platform: ROS2, C++
• Hardware: TurtleBot3 with LiDAR and wheel odometry
• Environment: Indoor, realistic navigation challenges

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Introduction

Simultaneous Localization and Mapping (SLAM) is crucial for enabling autonomous robots to navigate unknown environments.

Challenges Addressed:

1. Particle resampling inefficiencies in traditional RBPF SLAM.
2. Computational constraints in real-time indoor navigation.

Solution:

We propose adaptive sampling strategies:

• Dynamic Weight Normalization: Balances particle weights.
• Multi-Resolution Sampling: Allocates particles based on uncertainty.

Our methodology ensures accurate localization and mapping with reduced computational requirements.

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Core Algorithm

The SLAM problem is defined as:

$p(x_{1:t}, m | z_{1:t}, u_{1:t}) \propto p(x_{1:t} | z_{1:t}, u_{1:t}) \cdot p(m | x_{1:t}, z_{1:t})$

To minimize particle depletion:

1. Adaptive Thresholding dynamically adjusts the resampling trigger based on particle weight variance.
2. Multi-Resolution Sampling prioritizes high-uncertainty regions for particle allocation.

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Implementation

Setup

1. Hardware: TurtleBot3 (LiDAR and wheel odometry)
2. Software: ROS2, C++, Gazebo Simulation
3. Dependencies:
   • ROS2 Foxy or higher
   • TurtleBot3 packages
   • Gazebo (optional for simulation)

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Key Components:

• Particle Resampling: Improved weight normalization minimizes outlier effects.
• Map Update: Adaptive particle allocation for efficient grid-based mapping.
• Pose Estimation: EKF-inspired correction enhances robustness.

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Experimental Setup

Environment

• Narrow hallways
• Multi-room spaces
• Dynamic obstacles and occlusions

Results

Metric	Baseline RBPF SLAM	Proposed Method (Adaptive)
Mapping Accuracy	Lower	Higher
Particle Utilization	Inefficient	Optimized
Computational Cost	High	Reduced

Visual Comparison

Baseline RBPF SLAM:

Proposed Method:

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How to Run

1. Install Dependencies

Follow ROS2 installation guidelines: ROS2 Docs.

# Install TurtleBot3 and Gazebo dependencies
sudo apt update
sudo apt install ros-foxy-turtlebot3 ros-foxy-gazebo-ros-pkgs

2. Clone the Repository

git clone https://github.com/your-repo/offline-rbpf-slam-adaptive.git
cd offline-rbpf-slam-adaptive

3. Build the Workspace

colcon build
source install/setup.bash

4. Run the Algorithm

For real TurtleBot3:

ros2 launch offline_rbpf_slam adaptive_slam_launch.py

For Gazebo Simulation:

ros2 launch offline_rbpf_slam adaptive_slam_gazebo.launch.py

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Results

Our approach achieves:

1. Improved Mapping Accuracy with reduced particle count.
2. Reduced Computational Overhead via adaptive resampling.

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Future Work

1. Integration of GTSAM for nonlinear factor graph optimization.
2. Testing on larger and more complex environments.

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References

1. Grisetti, G., Stachniss, C., & Burgard, W. (2005). Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling.
2. Montemerlo, M., Thrun, S. (2003). FastSLAM: A Factored Solution to the SLAM Problem.
3. OpenSLAM.org: GMapping Package.