Comparison of modern open-source visual SLAM approaches

Sharafutdinov, Dinar; Griguletskii, Mark; Kopanev, Pavel; Kurenkov, Mikhail; Ferrer, Gonzalo; Burkov, Aleksey; Gonnochenko, Aleksei; Tsetserukou, Dzmitry

doi:10.48550/arxiv.2108.01654

Cited by 3 publications

(4 citation statements)

References 47 publications

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“…Another hurdle to overcome was finding methods that were open-source and readily deployable, an issue raised in Ref. [78]. The methods chosen were implemented on a Ubuntu 18.04 computer with an NVIDIA GeForce RTX 3070 GPU and CUDA 11.4.…”

Section: Representative Methodsmentioning

confidence: 99%

Benchmarking visual SLAM methods in mirror environments

Herbert,

Wu,

et al. 2024

Comp. Visual Media

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Visual simultaneous localisation and mapping (vSLAM) finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities, particularly mirror reflections. The effect of mirror presence (time visible and its average size in the frame) was hypothesised to impact localisation and mapping performance, with systems using direct techniques expected to perform worse. Thus, a dataset, MirrEnv, of image sequences recorded in mirror environments, was collected, and used to evaluate the performance of existing representative methods. RGBD ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration, whilst the remaining results did not show significantly degraded localisation performance. The mesh maps generated proved to be very inaccurate, with real and virtual reflections colliding in the reconstructions. A discussion is given of the likely sources of error and robustness in mirror environments, outlining future directions for validating and improving vSLAM performance in the presence of planar mirrors. The MirrEnv dataset is available at https://doi.org/10.17035/d.2023.0292477898.

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Section: Representative Methodsmentioning

confidence: 99%

Benchmarking visual SLAM methods in mirror environments

Herbert,

Wu,

et al. 2024

Comp. Visual Media

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“…For this reason, we compensated for the missing ground truth data using a sliding window average cubic polynomial fitting method, while we approximated the missing raw sensor measurements using a linear interpolation method. We do not have statistics of slam accuracy, but the comparison 4 shows that orb-slam3 exhibits performances similar to other state-of-the-art slam methods.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Estimation of the 3D velocity and/or position of a MAV is essential for both tele-operated and fully autonomous missions. Different sensing modalities can be used to this purpose, which can be roughly divided into three categories: External perception systems (UWB odometry, GPS, 1 Motion Capture system 2 ), Exteroceptive sensors (Lidar odometry, 3 visual odometry(VO) 4,5 ), and Proprioceptive sensors (inertial navigation system(INS) 1 ). Each category of sensors comes with its own advantages and drawbacks, especially in the context of MAV applications.…”

Section: Introductionmentioning

confidence: 99%

Airflow-based odometry for MAVs using thermal anemometers

Wang

Morin

2023

International Journal of Micro Air Vehicles

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This article concerns airflow-based odometry for estimating MAV flight speed from airflow measurements provided by a set of thermal anemometers. Our approach relies on a Gated Recurrent Unit (GRU) based deep learning approach to extract deep features from noisy and turbulent measurement signals of triaxial thermal anemometers, in order to establish the underlying mapping between the airflow measurement and the flight speed. The proposed solution is validated on a multi-rotor MAV. The results show that the GRU-based model can effectively extract noise features and perform denoising, and compensate for induced velocity effects along the propellers’ rotation axis. As a consequence, robust prediction of the flight speed is performed, including during takeoff and landing that induce ground effects and strong variations of vertical airflow.

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“…The wide variety of published visual or LiDAR SLAM approaches [6]- [8] might suggest that the SLAM problem could be considered solved. However, we argue that this is far from the truth, specifically when dealing with environments that are unstructured and severely aliased [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

Giubilato

Stürzl

Wedler

et al. 2022

IEEE Robot. Autom. Lett.

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We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) dataset, recorded on Mt. Etna, Sicily, an environment analogous to the Moon and Mars, using a handheld sensor suite with attributes suitable for implementation on a space-like mobile rover. The environment is characterized by challenging conditions regarding both the visual and structural appearance: severe visual aliasing poses significant limitations to the ability of visual SLAM systems to perform place recognition, while the absence of outstanding structural details, joined with the limited Field-of-View of the utilized Solid-State LiDAR sensor, challenges traditional LiDAR SLAM for the task of pose estimation using point clouds alone. With this data, that covers more than 4 kilometers of travel on soft volcanic slopes, we aim to: 1) provide a tool to expose limitations of state-ofthe-art SLAM systems with respect to environments, which are not present in widely available datasets and 2) motivate the development of novel localization and mapping approaches, that rely efficiently on the complementary capabilities of the two sensors. The dataset is accessible at the following url: https://rmc.dlr.de/s3li_dataset

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Comparison of modern open-source visual SLAM approaches

Cited by 3 publications

References 47 publications

Benchmarking visual SLAM methods in mirror environments

Benchmarking visual SLAM methods in mirror environments

Airflow-based odometry for MAVs using thermal anemometers

Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

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