Comparison of 3-dimensional SLAM systems: RTAB-Map vs. Kintinuous

Altuntas, Nihal; Uslu, Erkan; Çakmak, Furkan; Amasyalı, Mehmet Fatih; Yavuz, Sırma

doi:10.1109/ubmk.2017.8093567

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…Many algorithms are used for 3D mapping, including the widely used RTAB-Map algorithm. In [5], the authors present a comparative study of the trajectories generated by the RealSense D435 and Kinect V2 sensors using the RTAB-Map algorithm. The evaluation was performed using the Root Mean Square Error (RMSE) of Euclidean distances between true and estimated trajectories.…”

Section: Related Workmentioning

confidence: 99%

Kinect V2 vs Intel RealSense D435: A Comparative Study on 3D Mapping

Nascimento,

Melo,

Gil

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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The generation of accurate 3D maps is essential for an efficient autonomous navigation. With precise mapping, robots can plan optimal paths, avoiding obstacles and efficiently reaching their destinations. For this purpose, the use of RGB-D cameras, which capture color images and depth information of the environment, is common. This paper presents a comparative study of 3D maps generated by the Kinect V2 and RealSense D435 sensors, which were properly configured in a manipulator robot for controlled data acquisition, considering different environments and capture conditions. The RTAB-Map algorithm was used to process the data acquired by the sensors and generate the 3D maps. This analysis allows identifying which sensors are more suitable for each type of environment, as well as their limitations and advantages. Thus, this comparison helps select the best camera for each application and provides valuable insights for the development of more accurate and efficient applications.

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Section: Related Workmentioning

confidence: 99%

Kinect V2 vs Intel RealSense D435: A Comparative Study on 3D Mapping

Nascimento,

Melo,

Gil

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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“…The system uses the bag-of-words concept for loop closure detection by determining if the new image comes from a previously visited location or a new location; If the hypothesis of the new image is above a certain threshold, the new location will be added to the map as a new graph constraint. Then, in the background, the map graph is optimized to reduce the drift error in the overall map [15]. To achieve real-time performance for large scale environments, the system has a memory manager that limits and control the number of locations that are used for loop closure detection [4].…”

Section: Rtab-mapmentioning

confidence: 99%

Benchmark of Visual and 3D Lidar SLAM Systems in Simulation Environment for Vineyards

Hroob

Polvara

Mellado

et al. 2021

Towards Autonomous Robotic Systems

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“…If the hypothesis of the new image is above a certain threshold, the new location will be added to the map as a new graph constraint. Then, in the background, the map graph is optimized to reduce the drift error in the overall map [14]. To achieve real-time performance for large scale environments, the system has a memory manager that limits and control the number of locations that are used for loop closure detection [4].…”

Section: Slam Algorithmsmentioning

confidence: 99%

Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards

Hroob¹,

Polvara²,

Molina³

et al. 2021

Preprint

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In this work, we present a comparative analysis of the trajectories estimated from various Simultaneous Localization and Mapping (SLAM) systems in a simulation environment for vineyards. Vineyard environment is challenging for SLAM methods, due to visual appearance changes over time, uneven terrain, and repeated visual patterns. For this reason, we created a simulation environment specifically for vineyards to help studying SLAM systems in such a challenging environment. We evaluated the following SLAM systems: LIO-SAM, StaticMapping, ORB-SLAM2, and RTAB-MAP in four different scenarios. The mobile robot used in this study equipped with 2D and 3D lidars, IMU, and RGB-D camera (Kinect v2). The results show good and encouraging performance of RTAB-MAP in such an environment.

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Comparison of 3-dimensional SLAM systems: RTAB-Map vs. Kintinuous

Cited by 11 publications

References 12 publications

Kinect V2 vs Intel RealSense D435: A Comparative Study on 3D Mapping

Kinect V2 vs Intel RealSense D435: A Comparative Study on 3D Mapping

Benchmark of Visual and 3D Lidar SLAM Systems in Simulation Environment for Vineyards

Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards

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