Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

By Guillaume Bresson, Zayed Alsayed, Li Yu, Sebastien Glaser. 2017

782 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Like in aircraft, ADSs need to be designed with high redundancies that will minimize the chance of a catastrophic failure.”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “All the surveys are categorized into several sub-sections, including the overall [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], localization [14], [15], [16], [17], [18], [19],”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “Edge servers may also assist cooperation among multiple vehicles for SLAM, which is called as centralized SLAM [68].”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “Cartographer [121], a SLAM package from Google, improves performance by using submap and loop closure while supporting both 2-”
Multimodal End-to-End Autonomous Driving (2019, background): “on multimodal data [39], [40]); monocular depth estimation [41], [42], [43], [44]; stixel-world representation (based on stereo images [45], [46], [47], [48], monocular [49], [50], and multimodal [51]); as well as SLAM and place recognition [52], [53], [54], [55], [56], [57], [58], [59], [60], [61].”
SuMa++: Efficient LiDAR-based Semantic SLAM (2019, background): “Odometry estimation and SLAM are classical topics in robotics with a large body of scientific work covered by several overview articles [6], [7], [28].”
A Comprehensive Survey of Visual SLAM Algorithms (2022, positive): “Besides the proposed criteria, it is also necessary to characterize the scene and application, since some scenarios may present speciﬁc attributes that may imply speciﬁc evaluation criteria, such as the analysis presented in [33].”
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, neutral): “Surveys on recent SLAM methods have been done by [53,127].”
A survey of deep learning techniques for autonomous driving (2019, methodology): “rementally with the computation of the camera pose. These methods belong to the area of Simultaneous Localization and Mapping (SLAM). For a survey on classical SLAM techniques, we refer the reader to [63]. Neural networks such as PoseNet [64], VLocNet++ [65], or the approaches introduced in [66], [6”
Visual-Inertial Navigation: A Concise Review (2019, background): “As a special instance of the well-known SLAM problem, VINS researchers have been quickly building up a rich body of literature on top of SLAM [36].”
AI-IMU Dead-Reckoning (2019, background): “[9]–[12]. A. Relation to Previous Literature Autonomous vehicle must robustly self-localize with their embarked sensor suite which generally consists of odometers, IMUs, radars or LiDARs, and cameras [1,2,12]. Simultaneous Localization And Mapping based on inertial sensors, cameras, and/or LiDARs ha”
A review of multimodal image matching: Methods and applications (2021, background): “Reliable registration is the cornerstone of alleviating environmental and seasonal changes, and for long-term localization [508].”
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting (2023, background): “This excellent rendering performance is attributed to the efficient 3d Gaussian rendering pipeline and can be further applied to real-time downstream tasks, such as VR [4], robot navigation [6] and autonomous driving [2].”
A Theoretical Foundation of Network Localization and Navigation (2018, background): “2844553 as location-based services [10]–[12], rescue operations [13]–[15], autonomous vehicles [16]–[18], Internetof-Things [19]–[21], health monitoring [22]–[24], and crowdsensing [25]–[27].”
Autonomous Cars: Research Results, Issues, and Future Challenges (2019, background): “Other function-specific surveys include planning and motion control in autonomous cars [26], long-term maps’ constructions for autonomous cars [29] and visual perception in the autonomous car from both implementation and users’ perspectives [31], [32].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “SLAM comprises simultaneously estimating the state of a vehicle and constructing a map of the environment [304], which paves the way for achieving full autonomy in autonomous driving [305].”
6G and Beyond: The Future of Wireless Communications Systems (2020, background): “Autonomous vehicles and UAVs are some of the most promising cyber-physical systems in existence today [10], [11].”