PRBonn/MapClosures

Effectively Detecting Loop Closures using Point Cloud Density Maps

What it solves

MapClosures addresses the challenge of effectively detecting loop closures in robotic mapping. Loop closure detection is the process of recognizing when a robot has returned to a previously visited location to correct drift and improve map accuracy.

How it works

The system utilizes point cloud density maps to identify these loop closures. It provides a pipeline for processing point cloud data to detect when the robot's current view matches a previous state in the map.

Who it’s for

This tool is designed for robotics researchers and developers working on SLAM (Simultaneous Localization and Mapping) and point cloud processing.

Highlights

  • Provides both a C++ library for integration into larger projects and a Python API/CLI for easier usage.
  • Includes a visualizer to help users inspect loop closure detections.
  • Based on research presented at ICRA 2024.
  • Built upon and inspired by the KISS-ICP framework.

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