ConPR: Ongoing Construction Site Dataset for Place Recognition

TL;DR

This paper introduces ConPR, a multi-session dataset from an active construction site, designed to evaluate place recognition algorithms amidst dynamic terrain changes, supporting visual, LiDAR, and sensor fusion methods.

Contribution

The paper presents a novel dataset capturing ongoing construction site changes, addressing a gap in existing datasets that overlook terrain and infrastructure alterations for place recognition.

Findings

Dataset includes images, LiDAR, IMU data for dynamic environments

Supports evaluation of visual and LiDAR-based place recognition

Provides ground truth for range-based evaluation

Abstract

Place recognition, an essential challenge in computer vision and robotics, involves identifying previously visited locations. Despite algorithmic progress, challenges related to appearance change persist, with existing datasets often focusing on seasonal and weather variations but overlooking terrain changes. Understanding terrain alterations becomes critical for effective place recognition, given the aging infrastructure and ongoing city repairs. For real-world applicability, the comprehensive evaluation of algorithms must consider spatial dynamics. To address existing limitations, we present a novel multi-session place recognition dataset acquired from an active construction site. Our dataset captures ongoing construction progress through multiple data collections, facilitating evaluation in dynamic environments. It includes camera images, LiDAR point cloud data, and IMU data, enabling visual and LiDAR-based place recognition techniques, and supporting sensor fusion. Additionally, we provide ground truth information for range-based place recognition evaluation. Our dataset aims to advance place recognition algorithms in challenging and dynamic settings. Our dataset is available at https://github.com/dongjae0107/ConPR.