Robust SVSF-SLAM Algorithm for Unmanned Vehicle in Dynamic Environment

2018 ◽  
Author(s):  
Fethi Demim ◽  
Abdelkrim Nemra ◽  
Abdelghani Boucheloukh ◽  
Kahina Louadj ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Unmanned Vehicle ◽  
Slam Algorithm

A new adaptive smooth variable structure filter SLAM algorithm for unmanned vehicle

2017 ◽  
Author(s):  
Fethi Demim ◽  
Abdelghani Boucheloukh ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Variable Structure ◽  
Unmanned Vehicle ◽  
Slam Algorithm ◽  
Smooth Variable Structure Filter

scholarly journals Visual Simultaneous Localization and Mapping (vSLAM) of Driverless Car in GPS-Denied Areas

2021 ◽  
Vol 12 (1) ◽  
pp. 49
Author(s):  
Abira Kanwal ◽  
Zunaira Anjum ◽  
Wasif Muhammad
Keyword(s):  
Science Fiction ◽  
Urban Areas ◽  
Visual Information ◽  
Dynamic Environment ◽  
Simultaneous Localization And Mapping ◽  
Video Camera ◽  
Input Image ◽  
Localization And Mapping ◽  
Health Related ◽  
Slam Algorithm

A simultaneous localization and mapping (SLAM) algorithm allows a mobile robot or a driverless car to determine its location in an unknown and dynamic environment where it is placed, and simultaneously allows it to build a consistent map of that environment. Driverless cars are becoming an emerging reality from science fiction, but there is still too much required for the development of technological breakthroughs for their control, guidance, safety, and health related issues. One existing problem which is required to be addressed is SLAM of driverless car in GPS denied-areas, i.e., congested urban areas with large buildings where GPS signals are weak as a result of congested infrastructure. Due to poor reception of GPS signals in these areas, there is an immense need to localize and route driverless car using onboard sensory modalities, e.g., LIDAR, RADAR, etc., without being dependent on GPS information for its navigation and control. The driverless car SLAM using LIDAR and RADAR involves costly sensors, which appears to be a limitation of this approach. To overcome these limitations, in this article we propose a visual information-based SLAM (vSLAM) algorithm for GPS-denied areas using a cheap video camera. As a front-end process, features-based monocular visual odometry (VO) on grayscale input image frames is performed. Random Sample Consensus (RANSAC) refinement and global pose estimation is performed as a back-end process. The results obtained from the proposed approach demonstrate 95% accuracy with a maximum mean error of 4.98.

scholarly journals An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment

2021 ◽  
Vol 11 (4) ◽  
pp. 1828
Author(s):  
Yakun Wu ◽  
Li Luo ◽  
Shujuan Yin ◽  
Mengqi Yu ◽  
Fei Qiao ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Mobile Robots ◽  
Semantic Information ◽  
Dynamic Environment ◽  
High Energy ◽  
Significant Loss ◽  
Frame Rate ◽  
Energy Efficiency Improvement ◽  
Localization And Mapping ◽  
Slam Algorithm

The Simultaneous Localization and Mapping (SLAM) algorithm is a hotspot in robot application research with the ability to help mobile robots solve the most fundamental problems of “localization” and “mapping”. The visual semantic SLAM algorithm fused with semantic information enables robots to understand the surrounding environment better, thus dealing with complexity and variability of real application scenarios. DS-SLAM (Semantic SLAM towards Dynamic Environment), one of the representative works in visual semantic SLAM, enhances the robustness in the dynamic scene through semantic information. However, the introduction of deep learning increases the complexity of the system, which makes it a considerable challenge to achieve the real-time semantic SLAM system on the low-power embedded platform. In this paper, we realized the high energy-efficiency DS-SLAM algorithm on the Field Programmable Gate Array (FPGA) based heterogeneous platform through the optimization co-design of software and hardware with the help of OpenCL (Open Computing Language) development flow. Compared with Intel i7 CPU on the TUM dataset, our accelerator achieves up to 13× frame rate improvement, and up to 18× energy efficiency improvement, without significant loss in accuracy.

scholarly journals An Adaptive SVSF-SLAM Algorithm in Dynamic Environment for Cooperative Unmanned Vehicles

2019 ◽  
Vol 52 (15) ◽  
pp. 394-399 ◽  
Author(s):  
Fethi Demim ◽  
Abdelghani Boucheloukh ◽  
Abdelkrim Nemra ◽  
Elhaouari Kobzili ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Unmanned Vehicles ◽  
Slam Algorithm

RGB-D SLAM Algorithm Suitable for Dynamic Environment Based on Target Detection

2021 ◽  
Author(s):  
Xiaochuang Huo ◽  
Lei Zhang ◽  
Mingce Guo ◽  
Xiangrui Wu
Keyword(s):  
Target Detection ◽  
Dynamic Environment ◽  
Slam Algorithm

Improving RGB-D SLAM in dynamic environments using semantic aided segmentation

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Lhilo Kenye ◽  
Rahul Kala
Keyword(s):  
Search Algorithm ◽  
Dynamic Environment ◽  
Optimization Techniques ◽  
Working Environment ◽  
Binary Mask ◽  
Localization And Mapping ◽  
Dynamic Objects ◽  
Slam Algorithm ◽  
Indoor And Outdoor Environments ◽  
Static Background

Summary Most conventional simultaneous localization and mapping (SLAM) approaches assume the working environment to be static. In a highly dynamic environment, this assumption divulges the impediments of a SLAM algorithm that lack modules that distinctively attend to dynamic objects despite the inclusion of optimization techniques. This work exploits such environments and reduces the effects of dynamic objects in a SLAM algorithm by separating features belonging to dynamic objects and static background using a generated binary mask image. While the features belonging to the static region are used for performing SLAM, the features belonging to non-static segments are reused instead of being eliminated. The approach employs deep neural network or DNN-based object detection module to obtain bounding boxes and then generates a lower resolution binary mask image using depth-first search algorithm over the detected semantics, characterizing the segmentation of the foreground from the static background. In addition, the features belonging to dynamic objects are tracked into consecutive frames to obtain better masking consistency. The proposed approach is tested on both publicly available dataset as well as self-collected dataset, which includes both indoor and outdoor environments. The experimental results show that the removal of features belonging to dynamic objects for a SLAM algorithm can significantly improve the overall output in a dynamic scene.

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