GRACO: A Multimodal Dataset for Ground and Aerial Cooperative Localization and Mapping

Zhu, Yilin; Kong, Yang; Jie, Yingrui; Xu, Shiyou; Cheng, Hui

doi:10.1109/lra.2023.3234802

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…Notably, NTU-VIRAL dataset (Nguyen et al, 2022) and GRACO dataset (Zhu et al, 2023) have made pioneering contributions in presenting data of both LiDAR and camera from aerial perspectives. However, certain gaps persist in those datasets with LiDAR data, including low altitude (below 40 m), lack of natural surroundings, and small collection areas as they are all collected on university campuses.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, unlike previous aerial SLAM datasets which collect only visual-inertial data, including EuRoC (Burri et al, 2016), UPenn Fast Flight (Sun et al, 2018), Zurich Urban MAV dataset (Majdik et al, 2017), UZH-FPV dataset (Delmerico et al, 2019), Blackbird dataset (Antonini et al, 2018), CLOUD dataset (Patel et al, 2020), and WildNav dataset (Gurgu et al, 2022), we collect hardware-synchronized LiDAR, camera, IMU, and GNSS data for multi-sensor fusion. Different from previous aerial SLAM datasets which contain LiDAR data but were collected in small-scale school campuses at low altitudes, including NTU-VIRAL dataset (Nguyen et al, 2022) and GRACO dataset (Zhu et al, 2023), our dataset includes 21 sequences, captured across a variety of environments including an aero-model airfield, an island, a rural town, and a valley at an altitude of higher than 80 m. In these sequences, the established flight speeds range from 3 m/s to 12 m/s, covering an area of from 94,000 m 2 to 577,000 m 2 in a single flight. Moreover, our sensor package, configured for downward-looking orientation as shown in Figure 2, imposes considerable challenges for SLAM due to the downward-looking viewpoints and large-scale diversified environments.…”

Section: Related Workmentioning

confidence: 99%

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MARS-LVIG dataset: A multi-sensor aerial robots SLAM dataset for LiDAR-visual-inertial-GNSS fusion

Li,

Zou,

Chen

et al. 2024

The International Journal of Robotics Research

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In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (IMUs) and cameras for Simultaneous Localization and Mapping (SLAM) research. Public datasets covering different scenarios, platforms, and viewpoints are crucial for multi-sensor fusion SLAM studies, yet most focus on handheld or vehicle-mounted devices with front or 360-degree views. Data from aerial vehicles with downward-looking views is scarce, existing relevant datasets usually feature low altitudes and are mostly limited to small campus environments. To fill this gap, we introduce the Multi-sensor Aerial Robots SLAM dataset (MARS-LVIG dataset), providing unique aerial downward-looking LiDAR-Visual-Inertial-GNSS data with viewpoints from altitudes between 80 m and 130 m. The dataset not only offers new aspects to test and evaluate existing SLAM algorithms, but also brings new challenges which can facilitate researches and developments of more advanced SLAM algorithms. The MARS-LVIG dataset contains 21 sequences, acquired across diversified large-area environments including an aero-model airfield, an island, a rural town, and a valley. Within these sequences, the UAV has speeds varying from 3 m/s to 12 m/s, a scanning area reaching up to 577,000 m2, and the max path length of 7.148 km in a single flight. This dataset encapsulates data collected by a lightweight, hardware-synchronized sensor package that includes a solid-state 3D LiDAR, a global-shutter RGB camera, IMUs, and a raw message receiver of the Global Navigation Satellite System (GNSS). For algorithm evaluation, this dataset releases ground truth of both localization and mapping, which are acquired by on-board Real-time Kinematic (RTK) and DJI L1 (post-processed by its supporting software DJI Terra), respectively. The dataset can be downloaded from: https://mars.hku.hk/dataset.html .

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MARS-LVIG dataset: A multi-sensor aerial robots SLAM dataset for LiDAR-visual-inertial-GNSS fusion

Li,

Zou,

Chen

et al. 2024

The International Journal of Robotics Research

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“…Second, we split the very large (∼10km) KITTI360 09 lidar sequence [41] into 5 parts that contain a large number of loop closures, making it particularly well suited for interrobot loop closure detection analysis. Third, we experimented on the first three overlapping lidar sequences of the very recent GrAco dataset [42] acquired with custom ground robots on a college campus. Fourth, we evaluate our system on the three lidar Gate sequences of the M2DGR dataset [43].…”

Section: A Dataset Experimentsmentioning

confidence: 99%

Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems

Lajoie¹,

Beltrame²

2023

Preprint

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Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multirobot operations in environments without an external positioning system, such as indoors, underground or underwater. In this paper, we introduce Swarm-SLAM, an open-source C-SLAM system that is designed to be scalable, flexible, decentralized, and sparse, which are all key properties in swarm robotics. Our system supports inertial, lidar, stereo, and RGB-D sensing, and it includes a novel inter-robot loop closure prioritization technique that reduces communication and accelerates convergence. We evaluated our ROS-2 implementation on five different datasets, and in a real-world experiment with three robots communicating through an ad-hoc network.

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“…The NeBula [3] and CERBERUS [4] datasets are collected during the recent DARPA Subterranean Challenge. GRACO [31] includes multiple ground and aerial sequences for evaluating CSLAM using heterogeneous platforms. S3E [32] is a collection of CSLAM datasets that includes multiple indoor and outdoor trajectory designs with varying difficulties for 3 robots.…”

Section: Related Workmentioning

confidence: 99%

Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems

et al. 2022

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This paper revisits Kimera-Multi, a distributed multirobot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, this paper has three main contributions. First, we describe improvements to Kimera-Multi to make it resilient to large-scale real-world deployments, with particular emphasis on handling intermittent and unreliable communication. Second, we collect and release challenging multi-robot benchmarking datasets obtained during live experiments conducted on the MIT campus, with accurate reference trajectories and maps for evaluation. The datasets include up to 8 robots traversing long distances (up to 8 km) and feature many challenging elements such as severe visual ambiguities (e.g., in underground tunnels and hallways), mixed indoor and outdoor trajectories with different lighting conditions, and dynamic entities (e.g., pedestrians and cars). Lastly, we evaluate the resilience of Kimera-Multi under different communication scenarios, and provide a quantitative comparison with a centralized baseline system. Based on the results from both live experiments and subsequent analysis, we discuss the strengths and weaknesses of Kimera-Multi, and suggest future directions for both algorithm and system design. We release the source code of Kimera-Multi and all datasets to facilitate further research towards the reliable real-world deployment of multi-robot SLAM systems.

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GRACO: A Multimodal Dataset for Ground and Aerial Cooperative Localization and Mapping

Cited by 11 publications

References 31 publications

MARS-LVIG dataset: A multi-sensor aerial robots SLAM dataset for LiDAR-visual-inertial-GNSS fusion

MARS-LVIG dataset: A multi-sensor aerial robots SLAM dataset for LiDAR-visual-inertial-GNSS fusion

Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems

Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems

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