Dynamic Object Detection and Tracking in Construction: A Fisheye Camera and LiDAR Sensor Fusion Model

2026-07-08 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A novel framework addresses robust dynamic object detection and tracking for quadruped robots operating in complex construction environments. This system integrates a LiDAR sensor with an upward-facing fisheye camera to achieve real-time performance. The method first identifies moving objects within a registered point cloud. Subsequently, it assigns semantic labels by projecting 3D coordinates onto a 2D cylindrical panorama, which then aligns with real-time image-based detections to update a Kalman filter. This approach offers high precision, simplicity, and robustness, particularly in managing objects that transition between dynamic and static states. The proposed system is well-suited for deployment in demanding real-world construction settings, enhancing robot safety and operational effectiveness.

Key takeaway

For Robotics Engineers deploying autonomous systems in dynamic construction environments, this LiDAR and fisheye camera fusion framework offers a robust solution for real-time object detection and tracking. Your teams should evaluate this approach to enhance robot safety and operational effectiveness, especially when dealing with objects transitioning between static and dynamic states. This method's precision and simplicity can streamline integration into existing quadruped robot platforms.

Key insights

A novel sensor fusion model combines LiDAR and fisheye camera data for robust dynamic object detection and tracking in construction environments.

Principles

• Sensor fusion enhances robustness.
• 3D-to-2D projection assigns semantics.
• Kalman filters update observations.

Method

The method identifies moving objects in a registered point cloud, projects 3D coordinates to a 2D cylindrical panorama for semantic labeling, then aligns with image detections to update a Kalman filter.

In practice

• Deploy on quadruped robots.
• Apply in construction sites.
• Track dynamic/static objects.

Topics

• Sensor Fusion
• LiDAR
• Fisheye Cameras
• Dynamic Object Tracking
• Construction Robotics
• Kalman Filter

Best for: Research Scientist, Robotics Engineer, Computer Vision Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.