A systematic literature review on long‐term localization and mapping for mobile robots

Abstract

Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

A review of visual SLAM for robotics: evolution, properties, and future applications

“…Choosing the right visual SLAM algorithm is crucial for building an effective SLAM system. With the continuous advancements in V-SLAM methodologies responding to diverse challenges, it is essential to navigate structured criteria to deploy and implement precise solutions ( Placed et al, 2023 ; Sousa et al, 2023 ). In the context of robotic systems, we provide important parameters.…”

NeoSLAM: Long-Term SLAM Using Computational Models of the Brain

“…Although many studies have been conducted recently, there are still open questions regarding the long-term operation of robots [ 18 ], as can be seen in Figure 2 .…”

“…This dilemma refers to the challenge of finding the right balance between stability and plasticity in learning systems. Whereas stability refers to the ability of a system to maintain existing knowledge, plasticity refers to adapting and learning from new experiences [ 18 ].…”

Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on Edge Devices

“…A common requirement in these service robots is to be able to localize within their workspace, which is usually a man-made indoor environment [ 5 ]. Although precise position tracking can be provided by a SLAM (simultaneous localization and mapping) system using vision or RGB-D data, the issue of global localization remains a problem when the robot’s previous position data cannot be used [ 6 ]. Such a problem in practice arises, for example, in dynamic environments due to occlusions.…”