LiDAR Tabanlı SLAM: LeGo-LOAM ve HDL-Graph SLAM ile Karşılaştırmalı Bir Değerlendirme

Year 2025, Volume: 17 Issue: 1, 55 - 64

Yiğit Çağatay Kuyu , Fahri Vatansever

Abstract

Hızlı gelişmelerin yaşandığı eşzamanlı konum belirleme ve harita oluşturma (SLAM), araçların navigasyon ve algılama yeteneklerinin geliştirilmesinde önemli etkiye sahip olan ve karmaşık ortamlarda güvenli çalışmalarını sağlayan bir araştırma alanıdır. Bu çalışma, SLAM'deki son gelişmelere kapsamlı genel bakış sunmakta ve yaygın olarak kullanılan iki SLAM yönteminin karşılaştırmalı bir değerlendirmesini yapmaktadır. SLAM algoritmalarını değerlendirmek için en popüler kıyaslama veri kümelerinden biri olan KITTI veri kümesi kullanılmaktadır. Değerlendirme iki temel ölçüme odaklanmaktadır: Mutlak yörünge hatası (ATE) ve poz tahmininin zaman içindeki doğruluğu ve tutarlılığı hakkında değerli bilgiler sağlayan göreceli poz hatası (RPE). ATE, tahmin edilen yörüngeler ile yer referans verileri arasındaki sapmayı ölçerek SLAM sisteminin küresel doğruluğuna ışık tutarken RPE, yerel hatayı ve sistemin sıralı çerçeveler içinde güvenilir poz tahminlerini sürdürme yeteneğini inceler. Bu çalışma, her tekniğin avantajları, ayırt edici özellikleri ve performans özellikleri hakkında kapsamlı bir tartışma sağlanmakta ve böylece ilgili alanda gelecekteki araştırmaları ilerletmek için bilgiler sunulmaktadır.

Keywords

SLAM, LiDAR, LeGO-LOAM, HDL-Graph SLAM

LiDAR Based SLAM: A Comparative Evaluation with LeGO-LOAM and HDL-Graph SLAM

Abstract

Simultaneous localization and mapping (SLAM) is an area of research that is experiencing rapid advancements, with a significant impact on improving the navigation and perception capabilities of vehicles, thereby enabling their safe operation in complex environments. This study presents a comprehensive overview of the recent developments in SLAM and conducts a comparative evaluation of two widely employed SLAM methods. The evaluation is based on rigorous performance analysis using the KITTI dataset, which is one of the most popular benchmark datasets for evaluating SLAM algorithms. The evaluation focuses on two essential metrics: absolute trajectory error (ATE) and relative pose error (RPE), which provides valuable insights into the accuracy and consistency of pose estimation over time. By quantifying the deviation between estimated trajectories and ground truth data, ATE sheds light on the global accuracy of the SLAM system, while RPE examines the local error and the system's ability to maintain reliable pose estimates within sequential frames. A thorough discussion is provided on the advantages, distinctive features, and performance characteristics of each technique, thereby offering valuable insights to propel future research in this area.

Keywords

SLAM, LiDAR, LeGO-LOAM, HDL-Graph SLAM

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