Multi-camera visual SLAM for off-road navigation

Yang, Yi; Tang, Di; Wang, Dongsheng; Song, Wenjie; Wang, Junbo; Fu, Mengyin

doi:10.1016/j.robot.2020.103505

Cited by 43 publications

(18 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Commonly, such a strategy is exploited to estimate the optimal state. Such manipulation has enabled crucial advances in various navigation and intelligence-demanding tasks, such as perception [1,[5][6][7][8], object detection and tracking [9][10][11][12][13][14], semantic segmentation [15][16][17][18][19][20][21], and control [22][23][24] for AVs.…”

Section: Data Fusion Approachesmentioning

confidence: 99%

Context-Aware Sensor Uncertainty Estimation for Autonomous Vehicles

Alharbi

Karimi

2021

Vehicles

View full text Add to dashboard Cite

Sensor uncertainty significantly affects the performance of autonomous vehicles (AVs). Sensor uncertainty is predominantly linked to sensor specifications, and because sensor behaviors change dynamically, the machine learning approach is not suitable for learning them. This paper presents a novel learning approach for predicting sensor performance in challenging environments. The design of our approach incorporates both epistemic uncertainties, which are related to the lack of knowledge, and aleatoric uncertainties, which are related to the stochastic nature of the data acquisition process. The proposed approach combines a state-based model with a predictive model, where the former estimates the uncertainty in the current environment and the latter finds the correlations between the source of the uncertainty and its environmental characteristics. The proposed approach has been evaluated on real data to predict the uncertainties associated with global navigation satellite systems (GNSSs), showing that our approach can predict sensor uncertainty with high confidence.

show abstract

Section: Data Fusion Approachesmentioning

confidence: 99%

Context-Aware Sensor Uncertainty Estimation for Autonomous Vehicles

Alharbi

Karimi

2021

Vehicles

View full text Add to dashboard Cite

show abstract

“…The process from active environment perception and pose estimation back to autonomous navigation need different technologies. In the environment perception and pose estimation task, the simultaneous localization and mapping (SLAM) technology is one of the most effective methods [3], which can be divided into laser-slam [4,5] and visual-slam [6,7] according to the difference of sensors. And three-dimensional (3D) LIDAR such as Velodyne VLP-16 is widely used in outdoor environments, as it can not only provide an enormous amount of accurate distance data of 360 degrees around the vehicle, but also is insensitive to the changes in illumination.…”

Section: Introductionmentioning

confidence: 99%

An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles

Zhou

Zhang

et al. 2022

Robotica

View full text Add to dashboard Cite

At present, the study on autonomous unmanned ground vehicle navigation in an unstructured environment is still facing great challenges and is of great significance in scenarios where search and rescue robots, planetary exploration robots, and agricultural robots are needed. In this paper, we proposed an autonomous navigation method for unstructured environments based on terrain constraints. Efficient path search and trajectory optimization on octree map are proposed to generate trajectories, which can effectively avoid various obstacles in off-road environments, such as dynamic obstacles and negative obstacles, to reach the specified destination. We have conducted empirical experiments in both simulated and real environments, and the results show that our approach achieved superior performance in dynamic obstacle avoidance tasks and mapless navigation tasks compared to the traditional 2-dimensional or 2.5-dimensional navigation methods.

show abstract

“…With the rapid development of unmanned aerial vehicle (UAV) technologies such as aerial image stitching [1,2], Simultaneous Localization And Mapping (SLAM) [3,4], three-dimensional (3D) reconstruction [5], and behaviour analysis [6] have been widely developed and deployed. UAV has an increasingly important role in disaster relief, traffic monitoring and military surveillance.…”

Section: Introductionmentioning

confidence: 99%

Accurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging

Xie

Yang

Zhang

et al. 2021

IET Computer Vision

View full text Add to dashboard Cite

Small unmanned aerial vehicles (UAVs) have developed rapidly and are widely used for disaster relief, traffic monitoring and military surveillance. To perform these tasks better, it is necessary to improve the environmental perception ability of UAVs in a dynamic environment, including their static and dynamic perception ability. Specifically, both three-dimensional reconstruction for a static scene and localization for moving objects are required. Simultaneous Localization And Mapping technology has made great progress in static scene structure reconstruction and UAV self-motion estimation. However, accurate real-time localization of moving objects is still challenging. In this article, a global averaging based localization method is proposed to locate moving objects for a small UAV platform. Inspired by global structure from motion, this idea is applied to the localization of moving objects. To solve moving object localization, the relative motion estimation and global position optimisation methods are proposed. The proposed method was tested in various scenarios with a several trajectories. The extensive experimental results demonstrate the robustness and effectiveness of the proposed method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Multi-camera visual SLAM for off-road navigation

Cited by 43 publications

References 11 publications

Context-Aware Sensor Uncertainty Estimation for Autonomous Vehicles

Context-Aware Sensor Uncertainty Estimation for Autonomous Vehicles

An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles

Accurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging

Contact Info

Product

Resources

About