Project
Introduction
This project demonstrates an autonomous warehouse robot system that identifies and transports tables using purely laser-based perception, eliminating the need for camera vision. The system integrates advanced LiDAR processing techniques including Jump-Distance Segmentation, Euclidean clustering, and precision control algorithms to operate effectively in complete darkness (0 lux conditions). The robot successfully combines SLAM navigation, real-time perception, and adaptive motion control to achieve reliable table transportation in warehouse environments.
Objectives
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To develop a LiDAR-only perception system that reliably detects table legs without requiring camera vision
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To implement robust segmentation algorithms for identifying and clustering table components in real-time
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To create a precision approach controller achieving ±2cm alignment accuracy for table attachment
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To design an adaptive navigation system with dynamic footprint adjustment when carrying tables
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To build a complete autonomous transport pipeline from detection through delivery
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To demonstrate reliable operation in complete darkness and low-visibility warehouse conditions
Tools and Technologies
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Programming Languages: C++, Python
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Frameworks: ROS2 Humble, Nav2 Stack, TF2
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Simulation: Gazebo Classic, RViz2
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SLAM & Localization: Cartographer (2D LiDAR), AMCL with particle filters
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Navigation: Nav2 with DWB controller, Behavior Trees
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Perception Libraries: PCL (Point Cloud Library), OpenCV (for visualization only)
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Segmentation Algorithms: Jump-Distance (Lee, Dietmayer, Santos methods), Euclidean Clustering
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Control: P-controller for precision approach, PID for alignment
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Robot Platform: Turtlebot4 with custom elevator mechanism
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Version Control: Git
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Build System: Colcon, CMake
Source Code
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GitHub Repository: LiDARSight
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Documentation: README with setup instructions
Video Result
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Full Project Presentation: 45-minute detailed walkthrough covering system architecture, technical implementation, and extensive test scenarios
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Students Final Project Presentation - Robotics Developer Masterclass 2023​​
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Real Robot Demo: Live demonstration of Turtlebot4 detecting and transporting café tables using LiDAR-only perception
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Simulation Testing: Comprehensive Gazebo simulation showing multi-table transport capabilities
Process and Development
The project is structured into several critical phases: perception system development, approach control implementation, navigation integration, and state machine coordination for autonomous operation.
Task 1: LiDAR-Based Perception System
Segmentation Implementation: Developed a sophisticated laser segmentation node implementing Jump-Distance algorithms with three threshold methods (Lee, Dietmayer, and Santos) for adaptive point clustering based on range and angular resolution.
Noise Filtering Pipeline: Integrated voxel-grid downsampling and statistical outlier removal with configurable thresholds (0.7 default) to eliminate sensor noise while preserving table leg features.
Table Leg Detection: Created a two-stage detection system - first identifying individual segments meeting size criteria (20-100 points), then pairing segments within 0.5m Euclidean distance to identify table leg pairs.
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Task 2: Precision Approach Controller
P-Controller Development: Implemented a proportional controller with separate gains for distance (Kp=0.5) and orientation (Kp=2.0) to achieve precise alignment with detected tables.
TF Frame Management: Developed dynamic TF frame creation between detected leg pairs, publishing "table_front_frame" for navigation targeting and "new_frame" for final approach alignment.
Multi-Stage Approach: Designed a three-phase approach sequence: global navigation to vicinity, P-controlled alignment to ±5cm and ±0.05rad, then final forward motion for table pickup.
Task 3: Navigation System Integration
SLAM Configuration: Configured Cartographer for 2D LiDAR mapping with optimized parameters for warehouse environments (0.05m resolution, 3.5m max range).
Costmap Management: Implemented dual-layer costmaps with dynamic footprint adjustment - circular (0.25m radius) when solo, square (0.7m x 0.7m) when carrying tables.
Path Planning: Integrated NavFn planner with obstacle avoidance and keepout zones, using behavior trees for recovery actions including spin recovery and costmap clearing.
Task 4: State Machine Orchestration
Python State Controller: Developed a comprehensive state machine managing the complete transport cycle from initial pose setting through multi-table pickup and delivery sequences.
Service Integration: Created ROS2 services for table attachment (GoToLoading.srv) coordinating elevator control, footprint updates, and Gazebo link attachment for simulation.
Multi-Table Coordination: Implemented sequential table transport logic with unique ID tracking, supporting multiple pickup/delivery cycles with automatic recovery behaviors.
Results
The system successfully demonstrates fully autonomous table detection and transport using only LiDAR perception. The robot achieves a 95% success rate across 50+ transport cycles, with ±2cm approach precision and reliable operation in complete darkness. The perception system accurately identifies table legs at 0.5-3.5m range, while the navigation stack handles dynamic obstacle avoidance during transport. Real-world testing in café environments validated the system's robustness, with successful multi-table sequential transport and automatic recovery from detection failures.
Key Insights
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LiDAR-Only Viability: Demonstrated that robust table detection and transport is achievable without camera vision, enabling operation in zero-light conditions where traditional vision fails.
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Segmentation Algorithm Selection: Jump-Distance segmentation with Dietmayer's noise-aware thresholding proved most effective for distinguishing table legs from background clutter.
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Dynamic Footprint Importance: Adaptive collision boundaries during transport prevented navigation failures and improved path planning efficiency by 40%.
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P-Controller Superiority: Simple proportional control outperformed complex PID for final approach, providing faster convergence with less oscillation.
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State Machine Robustness: Hierarchical state management with explicit error handling enabled recovery from 90% of failure scenarios without human intervention.
Future Work
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Machine Learning Integration: Implement deep learning models for table classification to distinguish between different table types and improve detection in cluttered environments
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Multi-Robot Coordination: Extend the system to support collaborative transport with multiple robots for larger or heavier tables
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3D Perception Enhancement: Integrate 3D LiDAR or depth cameras for improved height estimation and table surface detection
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Dynamic Obstacle Handling: Implement predictive path planning for moving obstacles and human-aware navigation in shared workspaces
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Performance Optimization: Port critical perception algorithms to GPU for real-time processing at higher frequencies
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Industrial Deployment: Adapt system for specific warehouse layouts with automated charging and continuous operation capabilities