Multi-Sensor Mobile Robot Perception

Environment perception system based on binocular depth vision sensing and LiDAR for mobile robots

This project develops an advanced multi-sensor fusion system for mobile robot environment perception, combining binocular depth cameras, LiDAR, and inertial measurement units to achieve robust autonomous navigation and mapping capabilities.

The system addresses critical limitations of single-sensor approaches by integrating complementary sensing modalities, enabling reliable operation across diverse environmental conditions and scenarios.

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Project Overview

Mobile robot environment perception is fundamental to autonomous navigation systems. This comprehensive project integrates multiple sensor technologies on a tracked mobile robot platform to create a robust, multi-modal perception system capable of real-time simultaneous localization and mapping (SLAM).

Core Objectives:

  • Develop multi-sensor fusion architecture for enhanced environmental perception
  • Implement real-time data processing and sensor coordination
  • Achieve autonomous robot localization and environment mapping
  • Demonstrate practical applications in complex scenarios

Technical Architecture

Hardware Platform Configuration

The system is built upon a tracked mobile robot platform equipped with:

Processing Unit:

  • Raspberry Pi 4B as main computing platform
  • Robot Operating System (ROS) framework implementation
  • Linux-based distributed computing architecture

Sensor Suite:

  • Intel RealSense D435i binocular depth camera with IMU
  • LiDAR sensor for high-precision 3D geometric information
  • Monocular USB camera for visual data acquisition
  • Inertial Measurement Unit (IMU) for motion sensing and orientation

Multi-Sensor Integration Strategy

1. Spatial Coordinate Transformation

  • Sensor-to-world coordinate system conversion
  • Camera coordinate system alignment
  • Image coordinate system projection
  • Multi-sensor data fusion in unified coordinate space

2. Temporal Data Synchronization

  • Different sensor sampling frequency handling
  • Timestamp alignment across sensor modalities
  • Interpolation and multi-threading implementation
  • Real-time data stream coordination

3. Complementary Sensing Capabilities

  • Vision sensors: Rich semantic information, texture analysis
  • LiDAR: High-precision 3D geometry, lighting-independent operation
  • IMU: Motion estimation, orientation tracking, navigation assistance

Implementation Methodology

ROS System Development

The project utilizes a three-layer ROS architecture:

Operating System Layer: Linux kernel foundation providing memory management, process control, and network communication

Middleware Layer: ROS core communication mechanisms and robotics development libraries

Application Layer: Custom node development for sensor integration and algorithm implementation

Sensor Driver Development

LiDAR Integration:

  • Time-of-Flight technology implementation
  • 360° horizontal scanning capability
  • Real-time 3D environment model generation
  • Custom ROS topic development for data streaming

Vision System Implementation:

  • Binocular depth estimation and 3D reconstruction
  • Monocular camera integration for visual odometry
  • Real-time image processing and feature extraction
  • Multi-resolution support and parameter optimization

IMU Sensor Fusion:

  • 6-DoF motion and rotation detection
  • Accelerometer and gyroscope data integration
  • Navigation and positioning assistance
  • Sensor calibration and drift compensation

Advanced SLAM Implementation

LVI-SAM Algorithm Integration:

  • Lidar-Visual-Inertial simultaneous localization and mapping
  • Tightly-coupled multi-sensor fusion approach
  • Factor graph-based optimization framework
  • Real-time state estimation and robust mapping

Key Features:

  • Visual-inertial system initialization using LiDAR estimates
  • LiDAR measurement enhancement of visual feature depth
  • Visual loop closure detection integration
  • High-precision, real-time performance

ORB-SLAM2 Framework:

  • Support for monocular, stereo, and RGB-D camera modes
  • Real-time camera pose estimation
  • Sparse 3D environment reconstruction
  • CPU-based real-time loop closure detection and relocalization

Experimental Results and Performance

System Capabilities Demonstrated

Multi-Modal Data Acquisition:

  • Successful integration of four sensor types
  • Real-time data streaming and processing
  • Robust operation across lighting conditions
  • Accurate 3D environment reconstruction

SLAM Performance:

  • LVI-SAM: High-precision state estimation and mapping
  • ORB-SLAM2: Centimeter-level localization accuracy
  • Real-time loop closure detection
  • Sparse point cloud map generation

Autonomous Navigation:

  • Self-localization in unknown environments
  • Dynamic obstacle detection and avoidance
  • Incremental map building during exploration
  • Robust performance in complex scenarios

Technical Achievements

Hardware Integration:

  • Successful multi-sensor platform development
  • ROS topic drivers for all sensor modalities
  • Stable communication and data synchronization
  • Remote control and monitoring capabilities

Software Implementation:

  • Custom ROS node development
  • Multi-threaded sensor data processing
  • Real-time algorithm execution
  • Comprehensive system testing and validation

Innovation and Impact

Technical Contributions

Multi-Sensor Fusion Methodology:

  • Novel approach combining visual, LiDAR, and inertial sensing
  • Effective handling of sensor characteristic differences
  • Robust performance across environmental conditions
  • Scalable architecture for additional sensor integration

Cost-Effective Solution:

  • Raspberry Pi-based implementation
  • Open-source software framework utilization
  • Practical deployment considerations
  • Educational and research accessibility

Applications and Future Prospects

Autonomous Driving Research:

  • Advanced perception system development
  • Multi-modal sensor fusion validation
  • Real-world testing platform

Robotics and AI Development:

  • SLAM algorithm research and validation
  • Computer vision and sensor fusion studies
  • Educational robotics platform

Industrial Applications:

  • Warehouse automation and navigation
  • Inspection and monitoring systems
  • Search and rescue operations

Technical Specifications

Processing Performance:

  • Real-time multi-sensor data processing
  • Concurrent SLAM algorithm execution
  • Remote monitoring and control capabilities
  • Stable operation under computational constraints

Sensing Capabilities:

  • 360° LiDAR environmental scanning
  • Binocular depth estimation with IMU integration
  • Visual feature tracking and mapping
  • Multi-modal sensor calibration and synchronization

Navigation Performance:

  • Autonomous exploration and mapping
  • Real-time localization and path planning
  • Obstacle detection and avoidance
  • Loop closure detection and map optimization

This project demonstrates the successful integration of advanced robotics technologies, providing a foundation for further research in autonomous systems and multi-sensor perception applications.

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