VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM

Nguyen, Thien-Minh; Yuan, Shenghai; Cao, Muqing; Nguyen, Thien Hoang; Xie, Lihua

doi:10.48550/arxiv.2105.03296

Cited by 9 publications

(15 citation statements)

References 22 publications

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“…A further plan is to combine the camera with UWB/IMU/Odometer IPSs to enable mapping so that this system can operate well in harsh and unstructured environments. The fusion of these sensors can also overcome the problem of cameras failing to find feature points in single environments, leading to positioning failure [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System

Liu

Lin

Wang

et al. 2022

Sensors

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Ultra-wideband (UWB) is a promising indoor position technology with centimetre-level positioning accuracy in line-of-sight (LOS) situations. However, walls and other obstacles are common in an indoor environment, which can introduce non-line-of-sight (NLOS) and deteriorate UWB positioning accuracy to the meter level. This paper proposed a succinct method to identify NLOS induced by walls and mitigate the error for improved UWB positioning with NLOS. First, NLOS is detected by a sliding window method, which can identify approximately 90% of NLOS cases in a harsh indoor environment. Then, a delay model is designed to mitigate the error of the UWB signal propagating through a wall. Finally, all the distance measurements, including LOS and NLOS, are used to calculate the mobile UWB tag position with ordinary least squares (OLS) or weighted least squares (WLS). Experiment results show that with correct NLOS indentation and delay model, the proposed method can achieve positioning accuracy in NLOS environments close to the level of LOS. Compared with OLS, WLS can further optimise the positioning results. Correct NLOS indentation, accurate delay model and proper weights in the WLS are the keys to accurate UWB positioning in NLOS environments.

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Section: Discussionmentioning

confidence: 99%

A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System

Liu

Lin

Wang

et al. 2022

Sensors

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“…Sensor (1) eee (1) Sensors L,I,C are abbreviations of LiDAR, IMU, camera, respectively, L2 represents using two LiDAR sensors. (2) Results are from [51]. The horz.…”

Section: Methodsmentioning

confidence: 99%

Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM

Lang

et al. 2023

IEEE/ASME Trans. Mechatron.

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Localization and mapping with heterogeneous multisensor fusion have been prevalent in recent years. To adequately fuse multi-modal sensor measurements received at different time instants and different frequencies, we estimate the continuoustime trajectory by fixed-lag smoothing within a factor-graph optimization framework. With the continuous-time formulation, we can query poses at any time instants corresponding to the sensor measurements. To bound the computation complexity of the continuous-time fixed-lag smoother, we maintain temporal and keyframe sliding windows with constant size, and probabilistically marginalize out control points of the trajectory and other states, which allows preserving prior information for future sliding-window optimization. Based on continuoustime fixed-lag smoothing, we design tightly-coupled multi-modal SLAM algorithms with a variety of sensor combinations, like the LiDAR-inertial and LiDAR-inertial-camera SLAM systems, in which online timeoffset calibration is also naturally supported. More importantly, benefiting from the marginalization and our derived analytical Jacobians for optimization, the proposed continuous-time SLAM systems can achieve real-time performance regardless of the high complexity of continuoustime formulation. The proposed multi-modal SLAM systems have been widely evaluated on three public datasets and selfcollect datasets. The results demonstrate that the proposed continuous-time SLAM systems can achieve high-accuracy pose estimations and outperform existing state-of-the-art methods. To benefit the research community, we will open source our code at https://github.com/APRIL-ZJU/clic.

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“…The authors of [ 67 ] proposed a multi-modal system titled VIRAL (Visual-Inertial-Ranging-LiDAR) SLAM that couples camera, LiDAR, IMU, and UWB. They also presented a map-matching marginalization scheme for visual features based on the local map constructed from LiDAR point clouds.…”

Section: Vslam State-of-the-artsmentioning

confidence: 99%

Visual SLAM: What Are the Current Trends and What to Expect?

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

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In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them.

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VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM

Cited by 9 publications

References 22 publications

A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System

A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System

Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM

Visual SLAM: What Are the Current Trends and What to Expect?

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