A General Framework for Lifelong Localization and Mapping in Changing Environment

Zhao, Min; Guo, Xin; Song, Le; Qin, Bo; Shi, Xuesong; Lee, Gim Hee; Sun, Guanghui

doi:10.1109/iros51168.2021.9635985

Cited by 19 publications

(14 citation statements)

References 16 publications

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“…In 2021, Zhao Ming's team proposed a universal framework [11] that will be localized for life with the changing environment. They published their work which developed based on Google Cartographer.…”

Section: Visibility Based Methodsmentioning

confidence: 99%

Researches advanced in the SLAM for dynamic environment

2023

J. Phys.: Conf. Ser.

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Simultaneous Localization and Mapping (SLAM) has always been a popular topic in the computer vision community, as it seeks to forecast the location of agents and use sensors to detect their surroundings in order to construct maps and perform navigation. The majority of SLAM algorithms assume that objects in the environment are immobile or in slow motion, whereas the SLAM system in a dynamic world remains an unanswered question. When constructing a 3D point cloud map by sequentially accumulating scanning data, dynamic objects typically leave undesirable traces in the map. The traces of these dynamic items operate as impediments, hindering the positioning and navigation performance of mobile vehicles. In this work, we discuss the most recent advancements in SLAM in a dynamic context. Specifically, this paper examines three characteristics of the most significant negative effects that the dynamic environment has on SLAM. Then, according to the dynamic degree, solutions are presented for various dynamic items, including the design concept, basic framework, advantages, and disadvantages. We conclude by discussing the existing research difficulties in the subject of dynamic SLAM and its projected development path.

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Section: Visibility Based Methodsmentioning

confidence: 99%

Researches advanced in the SLAM for dynamic environment

2023

J. Phys.: Conf. Ser.

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“…A similar approach to reusing information about the environment's past configurations was presented by Mitsou and Tzafestas (2007). In recent years, we can observe a further development of similar methods; for example Zhao et al (2021). In this work, the authors approach the problem of localization similarly to Biber and Duckett (2005), but with a focus on decreasing the computational complexity.…”

Section: Localizationmentioning

confidence: 99%

Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

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“…Also, DXSLAM [78] has an increased robustness to changing environments, but fails in regular dynamic environments. The SLAM methods found in the literature that are robust to both dynamic and changing environments only perform 2D SLAM using LiDAR fused with other sensors such as IMU and odometry [67].…”

Section: Discussionmentioning

confidence: 99%

“…When it revisits the scene, some objects are in different locations, some are missing, and new objects may have appeared. This is often referred in the literature as SLAM in low dynamic environments [64][65], semi-static environments [66], changing environments [67], or simply long-term mapping [68].…”

Section: Visual Slam In Changing Environmentsmentioning

confidence: 99%

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Real-Time Metric-Semantic Visual Slam for Dynamic and Changing Environments

CARLOS VIRGOLINO SOARES

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A General Framework for Lifelong Localization and Mapping in Changing Environment

Cited by 19 publications

References 16 publications

Researches advanced in the SLAM for dynamic environment

Researches advanced in the SLAM for dynamic environment

Survey of maps of dynamics for mobile robots

Real-Time Metric-Semantic Visual Slam for Dynamic and Changing Environments

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