Mohammed CHGHAF

2025

1. MDPI

   

   Extended Study of a Multi-Modal Loop Closure Detection Framework for SLAM Applications

   Mohammed Chghaf, Sergio Rodriguez, and Abdelhafid El Ouardi

   Electronics, Jan 2025

   Abs DOI Bib PDF Website

   

   Loop Closure (LC) is a crucial task in Simultaneous Localization and Mapping (SLAM) for Autonomous Ground Vehicles (AGV). It is an active research area because it improves global localization efficiency. The consistency of the global map and the accuracy of the AGV’s location in an unknown environment are highly correlated with the efficiency and robustness of Loop Closure Detection (LCD), especially when facing environmental changes or data unavailability. We propose to introduce multimodal complementary data to increase the algorithms’ resilience. Various methods using different data sources have been proposed to achieve precise place recognition. However, integrating a multimodal loop-closure fusion process that combines multiple information sources within a SLAM system has been explored less. Additionally, existing multimodal place recognition techniques are often difficult to integrate into existing frameworks. In this paper, we propose a fusion scheme of multiple place recognition methods based on camera and LiDAR data for a robust multimodal LCD. The presented approach uses Similarity-Guided Particle Filtering (SGPF) to identify and verify candidates for loop closure. Based on the ORB-SLAM2 framework, the proposed method uses two perception sensors (camera and LiDAR) under two data representation models for each. Our experiments on both KITTI and a self-collected dataset show that our approach outperforms the state-of-the-art methods in terms of place recognition metrics or localization accuracy metrics. The proposed Multi-Modal Loop Closure (MMLC) framework enhances the robustness and accuracy of AGV’s localization by fusing multiple sensor modalities, ensuring consistent performance across diverse environments. Its real-time operation and early loop closure detection enable timely trajectory corrections, reducing navigation errors and supporting cost-effective deployment with adaptable sensor configurations.

   @article{chghaf:hal-03647769,
     title = {{Extended Study of a Multi-Modal Loop Closure Detection Framework for SLAM Applications}},
     author = {Chghaf, Mohammed and Rodriguez, Sergio and El Ouardi, Abdelhafid},
     url = {https://www.mdpi.com/2079-9292/14/3/421},
     journal = {{Electronics}},
     publisher = {{MDPI}},
     volume = {14},
     number = {3},
     pages = {421},
     year = {2025},
     month = jan,
     doi = {10.3390/electronics14030421},
     dimensions = {true},
   }

2023

1. RAS

   

   A Multimodal Loop Closure Fusion for Autonomous Vehicles SLAM

   Mohammed Chghaf, Sergio Rodriguez, and Abdelhafid El Ouardi

   Robotics and Autonomous Systems, Jan 2023

   Abs DOI Bib PDF Website

   

   Place recognition and loop closure detection are critical steps in the process of Simultaneous Localization and Mapping (SLAM). Indeed, the ability to determine whether an Autonomous Ground Vehicle (AGV) has returned to a previously visited place is highly important in the context of building a reliable SLAM system. In order to build a consistent global map and to localize the AGV with high confidence in an unknown environment, it is crucial to reduce the cumulative error generated by pose estimation. Although multiple approaches using various data sources have been proposed in order to provide an accurate pose estimation, fewer studies have focused on the integration of a multimodal process to detect loop closure. In this work, we present a novel approach to leverage multiple modalities for a robust and reliable loop closure detection. Our method is based on Similarity-Guided Particle Filtering (SGPF) for the search and validation of Loop Closure Candidates (LCCs). We validate the proposed Multimodal Loop Closure (MMLC) by using two perception modalities based on Bag-of-Words and Scan Context techniques for camera-based and LiDAR-based place recognition, respectively. The efficiency of our method has been evaluated on both KITTI and a self-collected dataset. Compared to the classical loop closure used in ORB-SLAM2, the suggested approach reduces the Absolute Trajectory Error (ATE) by up to 54% and the cumulative error during run-time by up to 62.63%. Finally, 100% of the loops are accurately detected and the ground truth distance between the current pose and the LC is less than 3m in 98% of the cases.

   @article{chghaf2023_ras,
     title = {{A Multimodal Loop Closure Fusion for Autonomous Vehicles SLAM}},
     author = {Chghaf, Mohammed and Rodriguez, Sergio and El Ouardi, Abdelhafid},
     url = {https://doi.org/10.1016/j.robot.2023.104446},
     journal = {{Robotics and Autonomous Systems}},
     publisher = {{Elsevier}},
     volume = {165},
     pages = {104446},
     year = {2023},
     issn = {0921-8890},
     doi = {10.1016/j.robot.2023.104446},
     keywords = {SLAM, Camera, LiDAR, Localization, Loop closure, Mapping, Fusion, Multimodal},
     dimensions = {true},
   }

2022

1. JINT

   

   Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey

   Mohammed Chghaf, Sergio Rodriguez, and Abdelhafid El Ouardi

   Journal of Intelligent and Robotic Systems, Apr 2022

   Abs DOI Bib PDF Website

   

   Simultaneous Localization and Mapping (SLAM) have been widely studied over the last years for autonomous vehicles. SLAM achieves its purpose by constructing a map of the unknown environment while keeping track of the location. A major challenge, which is paramount during the design of SLAM systems, lies in the efficient use of onboard sensors to perceive the environment. The most widely applied algorithms are camera-based SLAM and LiDAR-based SLAM. Recent research focuses on the fusion of camera-based and LiDAR-based frameworks that show promising results. In this paper, we present a study of commonly used sensors and the fundamental theories behind SLAM algorithms. The study then presents the hardware architectures used to process these algorithms and the performance obtained when possible. Secondly, we highlight state-of-the-art methodologies in each modality and in the multi-modal framework. A brief comparison followed by future challenges is then underlined. Additionally, we provide insights to possible fusion approaches that can increase the robustness and accuracy of modern SLAM algorithms; hence allowing the hardware-software co-design of embedded systems taking into account the algorithmic complexity and the embedded architectures and real-time constraints.

   @article{chghaf:hal-03647771,
     title = {{Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey}},
     author = {Chghaf, Mohammed and Rodriguez, Sergio and El Ouardi, Abdelhafid},
     url = {https://link.springer.com/article/10.1007/s10846-022-01582-8?noAccess=true},
     journal = {{Journal of Intelligent and Robotic Systems}},
     publisher = {{Springer Verlag}},
     volume = {105},
     number = {1},
     pages = {2},
     year = {2022},
     month = apr,
     doi = {10.1007/s10846-022-01582-8},
     hal_id = {hal-03647769},
     hal_version = {v1},
     dimensions = {true},
   }