InertialNet: Toward Robust SLAM via Visual Inertial Measurement

Liu, Tse-An Jacky; Lin, Huei-Yung; Lin, Wei-Yang

doi:10.1109/itsc.2019.8917003

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…In [ 22 ], Liu et al presented a methodology to make visual odometry more robust through the use of a deep network model to predict complex camera motion. Using the image input and IMU output as an end-to-end training pair makes data collection cost-effective.…”

Section: Related Workmentioning

confidence: 99%

Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

Prados

Lorente

Castro

2021

Sensors

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This paper presents a fully original algorithm of graph SLAM developed for multiple environments—in particular, for tunnel applications where the paucity of features and the difficult distinction between different positions in the environment is a problem to be solved. This algorithm is modular, generic, and expandable to all types of sensors based on point clouds generation. The algorithm may be used for environmental reconstruction to generate precise models of the surroundings. The structure of the algorithm includes three main modules. One module estimates the initial position of the sensor or the robot, while another improves the previous estimation using point clouds. The last module generates an over-constraint graph that includes the point clouds, the sensor or the robot trajectory, as well as the relation between positions in the trajectory and the loop closures.

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Section: Related Workmentioning

confidence: 99%

Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

Prados

Lorente

Castro

2021

Sensors

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“…SLAM: SLAM based on monocular camera are commonly used for environmental perception due to its low cost and convince, however it can't construct a true size map due to the lack of depth information. Classic monocular camera based SLAM methods include MonoSLAM [72], ORB-SLAM [73], ORB-SLAM v2 [74] and DTAM [75]; and recent algorithms include LSD-SLAM [76], SVO [77] and InertialNet [78].…”

Section: B) Applicationmentioning

confidence: 99%

A Survey on Sensor Technologies for Unmanned Ground Vehicles

Liu

Yuan

2020

2020 3rd International Conference on Unmanned Systems (ICUS)

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Unmanned ground vehicles (UGVs) have a huge development potential in both civilian and military fields, and have become the focus of research in various countries. In addition, high-precision, high-reliability sensors are significant for UGVs' efficient operation. This paper proposes a brief review on sensor technologies for UGVs. Firstly, characteristics of various sensors are introduced. Then the strengths and weaknesses of different sensors as well as their application scenarios are compared. Furthermore, sensor applications in some existing UGVs are summarized. Finally, the hotspots of sensor technologies are forecasted to point the development direction.

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“…It can be used on micro aerial vehicles to achieve positioning and navigation [14]. It can also be used to implement the robot simultaneous localization and mapping (SLAM) function [15]. The use of VIO technology for indoor positioning has attracted the attention of many scholars in recent years [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Zhang

Ren

Wang

et al. 2020

Sensors

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High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.

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InertialNet: Toward Robust SLAM via Visual Inertial Measurement

Cited by 7 publications

References 10 publications

Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

A Survey on Sensor Technologies for Unmanned Ground Vehicles

Indoor Localization Based on VIO System and Three-Dimensional Map Matching

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