The Challenge of Semantic Symmetry in Visualization

Goebel, Randy; Shi, Wei; Tanaka, Yuzuru

doi:10.1109/iv.2014.32

Cited by 2 publications

(1 citation statement)

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“…RTAB-Map, for Real-Time Appearance-Based Mapping 1 Michaud, 2013, Labbé and, is our open source library implementing loop closure detection with a memory management approach, limiting the size of the map so that loop closure detections are always processed under a fixed time limit, thus satisfying online requirements for long-term and large-scale environment mapping. Initiated in 2009 and released as an open source library in 2013, RTAB-Map has since be extended to a complete graphbased SLAM approach [Stachniss et al, 2016] to be used in various setups and applications [Laniel et al, 2017, Foresti et al, 2016, Chen et al, 2015, Goebel, 2014. As a result, RTAB-Map has evolved into a cross-platform standalone C++ library and a ROS package 2 , driven by practical requirements such as:…”

Section: Introductionmentioning

confidence: 99%

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

Labbé

Michaud

2018

Journal of Field Robotics

683

325

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Distributed as an open-source library since 2013, real-time appearance-based mapping (RTAB-Map) started as an appearance-based loop closure detection approach with memory management to deal with large-scale and long-term online operation. It then grew to implement simultaneous localization and mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of constraints on sensors, processing capabilities, and locomotion, it raises the question of which SLAM approach is the most appropriate to use in terms of cost, accuracy, computation power, and ease of integration. Since most of SLAM approaches are either visual-or lidar-based, comparison is difficult. Therefore, we decided to extend RTAB-Map to support both visual and lidar SLAM, providing in one package a tool allowing users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. This paper presents this extended version of RTAB-Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real-world datasets (e.g., KITTI, EuRoC, TUM RGB-D, MIT Stata Center on PR2 robot), outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications. K E Y W O R D S perception, position estimation, SLAM

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