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

Yiğit Çağatay Kuyu; Fahri Vatansever

Makale detayları

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

Yazarlar:

Yiğit Çağatay Kuyu | Fahri Vatansever

Yayıncı:

Kirikkale University

Ana Başlıklar:

Information Systems (Other) | Electronics | Sensors and Digital Hardware (Other)

Dergi:

International Journal of Engineering Research and Development Vol.17 Issue 1, pp.55-64

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339427

pISSN:

1308-5506

Anahtar kelimeler:

Özet:

 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 systems 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.

 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 systems 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.

 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 systems 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.

 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, SLAMdeki 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.

Makale detayları

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

Yazarlar:

Yiğit Çağatay Kuyu | Fahri Vatansever

Yayıncı:

Kirikkale University

Ana Başlıklar:

Information Systems (Other) | Electronics | Sensors and Digital Hardware (Other)

Dergi:

International Journal of Engineering Research and Development Vol.17 Issue 1, pp.55-64

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339427

pISSN:

1308-5506

SLAM

LiDAR

LeGO-LOAM

HDL-Graph SLAMSLAM

LiDAR

LeGO-LOAM

HDL-Graph SLAM

Sayı

Makale detayları

Yazarlar: Yiğit Çağatay Kuyu | Fahri Vatansever

Yayıncı: Kirikkale University

Ana Başlıklar: Information Systems (Other) | Electronics | Sensors and Digital Hardware (Other)

Dergi: International Journal of Engineering Research and Development Vol.17 Issue 1, pp.55-64

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001339427

pISSN: 1308-5506

Sayı

Yazarlar:

Yiğit Çağatay Kuyu | Fahri Vatansever

Yayıncı:

Kirikkale University

Ana Başlıklar:

Information Systems (Other) | Electronics | Sensors and Digital Hardware (Other)

Dergi:

International Journal of Engineering Research and Development Vol.17 Issue 1, pp.55-64

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339427

pISSN:

1308-5506