RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Labbé, Mathieu; Michaud, François

doi:10.1002/rob.21831

Cited by 710 publications

(399 citation statements)

References 81 publications

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“…Each robot collects images from an onboard stereo camera and uses a (single-robot) Stereo Visual Odometry module to produce an estimate of its trajectory. In our implementation, we use the stereo odometry from RTAB-Map [45]. The images are also fed to the Distributed Loop Closure Detection module (Section III-A) which communicates information with other robots (when they are within communication range) and outputs inter-robot loop closure measurements.…”

Section: The Door-slam Systemmentioning

confidence: 99%

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Lajoie

Ramtoula

Chang

et al. 2020

IEEE Robot. Autom. Lett.

183

104

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To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop closure candidates, which results in less accurate trajectory estimates. This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters. DOOR-SLAM is based on peerto-peer communication and does not require full connectivity among the robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined with a distributed pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures; and a distributed SLAM front-end that detects inter-robot loop closures without exchanging raw sensor data. The system has been evaluated in simulations, benchmarking datasets, and field experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM produces more inter-robot loop closures, successfully rejects outliers, and results in accurate trajectory estimates, while requiring low communication bandwidth. Full source code is available at https://github.com/MISTLab/DOOR-SLAM.git.

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Section: The Door-slam Systemmentioning

confidence: 99%

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Lajoie

Ramtoula

Chang

et al. 2020

IEEE Robot. Autom. Lett.

183

104

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“…where (x, y) is the position and θ the orientation. This step can exploit any range-based or visual-based localization/SLAM algorithm, notably the provided framework supports and was tested with techniques already available on ROS as Gmapping SLAM [34], AMCL [35] and RTAB-Map library [36] which was initially developed for appearance-based loop closing and memory handling for large-scale scene mapping. These libraries provide localization and mapping techniques for several sensory modalities, including RGB-D, stereo or monocular camera settings for both 2D grid-based representation and the 3D textured point cloud of the scene.…”

Section: Localization and Mappingmentioning

confidence: 99%

Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework Using Visual and Depth Cues

Martins

Bersan

Campos

et al. 2020

J Intell Robot Syst

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This paper addresses the problem of building augmented metric representations of scenes with semantic information from RGB-D images. We propose a complete framework to create an enhanced map representation of the environment with object-level information to be used in several applications such as human-robot interaction, assistive robotics, visual navigation, or in manipulation tasks. Our formulation leverages a CNN-based object detector (Yolo) with a 3D model-based segmentation technique to perform instance semantic segmentation, and to localize, identify, and track different classes of objects in the scene. The tracking and positioning of semantic classes is done with a dictionary of Kalman filters in order to combine sensor measurements over time and then providing more accurate maps. The formulation is designed to identify and to disregard dynamic objects in order to obtain a mediumterm invariant map representation. The proposed method was evaluated with collected and publicly available RGB-D data sequences acquired in different indoor scenes. Experimental results show the potential of the technique to produce augmented semantic maps containing several objects (notably doors). We also provide to the community a dataset composed of annotated object classes (doors, fire extinguishers, benches, water fountains) and their positioning, as well as the source code as ROS packages. 1 1 Preprint paper version to appear at Journal of Intelligent & Robotic Systems, available online at: https://doi.

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“…A single sensor has unique advantages and limitations. Combining multiple sensors can effectively improve the performance of a SLAM system [15]. The commonly used sensor configuration is visual plus IMU, which results in visual inertial SLAM (VI-SLAM).…”

Section: Sensor Fusionmentioning

confidence: 99%

DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot

Yang¹,

Bi²,

Wang³

et al. 2019

Remote Sensing

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The state-of-the-art visual simultaneous localization and mapping (V-SLAM) systems have high accuracy localization capabilities and impressive mapping effects. However, most of these systems assume that the operating environment is static, thereby limiting their application in the real dynamic world. In this paper, by fusing the information of an RGB-D camera and two encoders that are mounted on a differential-drive robot, we aim to estimate the motion of the robot and construct a static background OctoMap in both dynamic and static environments. A tightly coupled feature-based method is proposed to fuse the two types of information based on the optimization. Dynamic pixels occupied by dynamic objects are detected and culled to cope with dynamic environments. The ability to identify the dynamic pixels on both predefined and undefined dynamic objects is available, which is attributed to the combination of the CPU-based object detection method and a multiview constraint-based approach. We first construct local sub-OctoMaps by using the keyframes and then fuse the sub-OctoMaps into a full OctoMap. This submap-based approach gives the OctoMap the ability to deform, and significantly reduces the map updating time and memory costs. We evaluated the proposed system in various dynamic and static scenes. The results show that our system possesses competitive pose accuracy and high robustness, as well as the ability to construct a clean static OctoMap in dynamic scenes.

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RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Cited by 710 publications

References 81 publications

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework Using Visual and Depth Cues

DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot

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