Related papers: A LiDAR-Inertial-Visual SLAM System with Loop Dete…
The SLAM system based on static scene assumption will introduce huge estimation errors when moving objects appear in the field of view. This paper proposes a novel multi-object dynamic lidar odometry (MLO) based on semantic object detection…
In this paper, we present a tightly-coupled visual-inertial object-level multi-instance dynamic SLAM system. Even in extremely dynamic scenes, it can robustly optimise for the camera pose, velocity, IMU biases and build a dense 3D…
Autonomous robots use simultaneous localization and mapping (SLAM) for efficient and safe navigation in various environments. LiDAR sensors are integral in these systems for object identification and localization. However, LiDAR systems…
Positioning is a prominent field of study, notably focusing on Visual Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM) methods. Despite their advancements, these methods often encounter dead-reckoning errors that…
Robust and fast motion estimation and mapping is a key prerequisite for autonomous operation of mobile robots. The goal of performing this task solely on a stereo pair of video cameras is highly demanding and bears conflicting objectives:…
Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environment. Incorporating semantics provides a richer scene…
In this paper, we consider the problems in the practical application of visual simultaneous localization and mapping (SLAM). With the popularization and application of the technology in wide scope, the practicability of SLAM system has…
In this letter, we propose a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses measurement from LiDAR, inertial sensor, and visual camera to achieve robust and accurate state estimation. Our proposed framework is…
Distributed as an open source library since 2013, 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…
The robustness of event cameras to high dynamic range and motion blur holds the potential to improve visual odometry systems in challenging environments. Although their high temporal resolution does not require synchronous processing, most…
A swarm of robots has advantages over a single robot, since it can explore larger areas much faster and is more robust to single-point failures. Accurate relative positioning is necessary to successfully carry out a collaborative mission…
Visible light positioning (VLP) technology is a promising technique as it can provide high accuracy positioning based on the existing lighting infrastructure. However, existing approaches often require dense lighting distributions.…
While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised. Sparse visual SLAM…
Monocular visual simultaneous localization and mapping (V-SLAM) is nowadays an irreplaceable tool in mobile robotics and augmented reality, where it performs robustly. However, human colonoscopies pose formidable challenges like occlusions,…
Simultaneous Localization and Mapping (SLAM) is a key tool for monitoring construction sites, where aligning the evolving as-built state with the as-planned design enables early error detection and reduces costly rework. LiDAR-based SLAM…
Advancing maturity in mobile and legged robotics technologies is changing the landscapes where robots are being deployed and found. This innovation calls for a transformation in simultaneous localization and mapping (SLAM) systems to…
In the realm of robotics, achieving simultaneous localization and mapping (SLAM) is paramount for autonomous navigation, especially in challenging environments like texture-less structures. This paper proposed a factor-graph-based model…
Visual-inertial simultaneous localization and mapping (SLAM) is a key module of robotics and low-speed autonomous vehicles, which is usually limited by the high computation burden for practical applications. To this end, an innovative…
This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image are…
Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical. It not only improves the performance but also enables tasks specified in terms of meaningful objects. This work presents…