LIV-LAM: LiDAR and Visual Localization and Mapping

Radmanesh, Reza; Wang, Ziyin; Chipade, Vishnu S.; Tsechpenakis, Gavriil; Panagou, Dimitra

doi:10.23919/acc45564.2020.9148037

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…LIMO [27] levers the power of deep learning to remove features on dynamic objects. LIV-LAM [78] proposes unsupervised learning for object discovery and uses detected features of the objects as landmark features.…”

Section: Loosely-coupled Methodsmentioning

confidence: 99%

Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey

Zhu,

Li,

Zhang

2024

Tsinghua Sci. Technol.

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In recent years, Simultaneous Localization And Mapping (SLAM) technology has prevailed in a wide range of applications, such as autonomous driving, intelligent robots, Augmented Reality (AR), and Virtual Reality (VR).Multi-sensor fusion using the most popular three types of sensors (e.g., visual sensor, LiDAR sensor, and IMU) is becoming ubiquitous in SLAM, in part because of the complementary sensing capabilities and the inevitable shortages (e.g., low precision and long-term drift) of the stand-alone sensor in challenging environments. In this article, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work. Firstly, a brief introduction of the state estimator formation in SLAM is presented. Secondly, the state-of-the-art algorithms of different multi-sensor fusion algorithms are given. Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations. This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.

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Section: Loosely-coupled Methodsmentioning

confidence: 99%

Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey

Zhu,

Li,

Zhang

2024

Tsinghua Sci. Technol.

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“…In addition to the current dominant multi-sensor fusion method based on the ORB-SLAM framework, there are also many excellent fusion methods worthy of reference and research. R. Radmanesh et al [193] proposed a monocular SLAM method based on light detection and LIDAR ranging to provide depth information, which uses camera data to process unknown objects in an unsupervised way, as well as visually detected features as landmark features, and fuses them with LIDAR sensor data [194]. The proposed method is superior to the current maps generated only by LIDAR in terms of computational efficiency and accuracy.…”

Section: (B) Other Fusion Optionsmentioning

confidence: 99%

SLAM Overview: From Single Sensor to Heterogeneous Fusion

et al. 2022

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After decades of development, LIDAR and visual SLAM technology has relatively matured and been widely used in the military and civil fields. SLAM technology enables the mobile robot to have the abilities of autonomous positioning and mapping, which allows the robot to move in indoor and outdoor scenes where GPS signals are scarce. However, SLAM technology relying only on a single sensor has its limitations. For example, LIDAR SLAM is not suitable for scenes with highly dynamic or sparse features, and visual SLAM has poor robustness in low-texture or dark scenes. However, through the fusion of the two technologies, they have great potential to learn from each other. Therefore, this paper predicts that SLAM technology combining LIDAR and visual sensors, as well as various other sensors, will be the mainstream direction in the future. This paper reviews the development history of SLAM technology, deeply analyzes the hardware information of LIDAR and cameras, and presents some classical open source algorithms and datasets. According to the algorithm adopted by the fusion sensor, the traditional multi-sensor fusion methods based on uncertainty, features, and novel deep learning are introduced in detail. The excellent performance of the multi-sensor fusion method in complex scenes is summarized, and the future development of multi-sensor fusion method is prospected.

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“…If a loop is detected, it is added as an edge to the pose graph. LIV-LAM [7] integrates LiDAR-based odometry measurements with target detection based on a monocular camera and associates it with loop closure detection through pose graph optimization. LC-LVF [8] suggests a new error function that takes both scanning and image data as constraints for pose graph optimization and uses g2o for further optimization, ultimately employing a Bag-of-Words-based method for revisited place detection.…”

Section: Introductionmentioning

confidence: 99%

Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Li,

Cui,

Wang

et al. 2023

Remote Sensing

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Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.

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LIV-LAM: LiDAR and Visual Localization and Mapping

Cited by 7 publications

References 23 publications

Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey

Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey

SLAM Overview: From Single Sensor to Heterogeneous Fusion

Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

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