scholarly journals Semantics Aware Dynamic SLAM Based on 3D MODT

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6355
Author(s):  
Muhammad Sualeh ◽  
Gon-Woo Kim
Keyword(s):  
Real Time ◽  
Semantic Information ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Detection And Tracking ◽  
Localization And Mapping ◽  
Fair Comparison ◽  
Dynamic Regions ◽  
Computational Resources ◽  
Static World

The idea of SLAM (Simultaneous Localization and Mapping) being a solved problem revolves around the static world assumption, even though autonomous systems are gaining environmental perception capabilities by exploiting the advances in computer vision and data-driven approaches. The computational demands and time complexities remain the main impediment in the effective fusion of the paradigms. In this paper, a framework to solve the dynamic SLAM problem is proposed. The dynamic regions of the scene are handled by making use of Visual-LiDAR based MODT (Multiple Object Detection and Tracking). Furthermore, minimal computational demands and real-time performance are ensured. The framework is tested on the KITTI Datasets and evaluated against the publicly available evaluation tools for a fair comparison with state-of-the-art SLAM algorithms. The results suggest that the proposed dynamic SLAM framework can perform in real-time with budgeted computational resources. In addition, the fused MODT provides rich semantic information that can be readily integrated into SLAM.

scholarly journals Real-Time Audio-Visual Analysis for Multiperson Videoconferencing

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Petr Motlicek ◽  
Stefan Duffner ◽  
Danil Korchagin ◽  
Hervé Bourlard ◽  
Carl Scheffler ◽  
...  
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Semantic Information ◽  
Visual Analysis ◽  
State Of The Art ◽  
Spatial Audio ◽  
Detection And Tracking ◽  
Video Objects ◽  
Object Coding ◽  
Virtual Director

We describe the design of a system consisting of several state-of-the-art real-time audio and video processing components enabling multimodal stream manipulation (e.g., automatic online editing for multiparty videoconferencing applications) in open, unconstrained environments. The underlying algorithms are designed to allow multiple people to enter, interact, and leave the observable scene with no constraints. They comprise continuous localisation of audio objects and its application for spatial audio object coding, detection, and tracking of faces, estimation of head poses and visual focus of attention, detection and localisation of verbal and paralinguistic events, and the association and fusion of these different events. Combined all together, they represent multimodal streams with audio objects and semantic video objects and provide semantic information for stream manipulation systems (like a virtual director). Various experiments have been performed to evaluate the performance of the system. The obtained results demonstrate the effectiveness of the proposed design, the various algorithms, and the benefit of fusing different modalities in this scenario.

Smartphone-Based Indoor Visual Navigation with Leader-Follower Mode

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Jingao Xu ◽  
Erqun Dong ◽  
Qiang Ma ◽  
Chenshu Wu ◽  
Zheng Yang
Keyword(s):  
Real Time ◽  
Environmental Changes ◽  
State Of The Art ◽  
Visual Navigation ◽  
Indoor Navigation ◽  
Location Services ◽  
Localization And Mapping ◽  
Leaders And Followers ◽  
Indoor Navigation System ◽  
Free Pair

Existing indoor navigation solutions usually require pre-deployed comprehensive location services with precise indoor maps and, more importantly, all rely on dedicatedly installed or existing infrastructure. In this article, we present Pair-Navi, an infrastructure-free indoor navigation system that circumvents all these requirements by reusing a previous traveler’s (i.e., leader) trace experience to navigate future users (i.e., followers) in a Peer-to-Peer mode. Our system leverages the advances of visual simultaneous localization and mapping ( SLAM ) on commercial smartphones. Visual SLAM systems, however, are vulnerable to environmental dynamics in the precision and robustness and involve intensive computation that prohibits real-time applications. To combat environmental changes, we propose to cull non-rigid contexts and keep only the static and rigid contents in use. To enable real-time navigation on mobiles, we decouple and reorganize the highly coupled SLAM modules for leaders and followers. We implement Pair-Navi on commodity smartphones and validate its performance in three diverse buildings and two standard datasets (TUM and KITTI). Our results show that Pair-Navi achieves an immediate navigation success rate of 98.6%, which maintains as 83.4% even after 2 weeks since the leaders’ traces were collected, outperforming the state-of-the-art solutions by >50%. Being truly infrastructure-free, Pair-Navi sheds lights on practical indoor navigations for mobile users.

Platform Protection Through Acoustic Signature Management

2006 ◽  
Author(s):  
Mae L. Seto
Keyword(s):  
Real Time ◽  
Dynamic Range ◽  
State Of The Art ◽  
Detection Range ◽  
Commanding Officer ◽  
Sensor Performance ◽  
Detection And Tracking ◽  
Processing Power ◽  
Acoustic Signature ◽  
The Impact

A naval ship's acoustic signature is known after a ranging but changes the longer it is in-service away from a range. The Ship Signatures Management System (SSMS) provides an organic real-time capability to predict their own signature and enough information to mitigate signature issues. SSMS provides the Commanding Officer with a tool to determine the ship's acoustic signature in order to evaluate the impact of his/her proposed actions on the ship's counter-detection range and sensor performance. In this manner, the ship's protection is enhanced through insightful and timely signature management. DRDC has upgraded the SSMS hardware to state-of-the-art components to increase the number of sensors, the fidelity of the logged data, the dynamic range, and the processing power. This paper discusses some of the advanced SSMS features developed like tonal detection and tracking, tonal association, and the diagnostics used to determine the cause of features in the acoustic signature.

scholarly journals LiDAR Odometry and Mapping Based on Semantic Information for Outdoor Environment

2021 ◽  
Vol 13 (15) ◽  
pp. 2864
Author(s):  
Shitong Du ◽  
Yifan Li ◽  
Xuyou Li ◽  
Menghao Wu
Keyword(s):  
Feature Extraction ◽  
Semantic Information ◽  
State Of The Art ◽  
Outdoor Environment ◽  
Geometric Features ◽  
Dynamic Object ◽  
Object Removal ◽  
Localization And Mapping ◽  
High Level ◽  
Crucial Part

Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.

scholarly journals Distributional Metareasoning for Heuristic Search

2021 ◽  
Author(s):  
Tianyi Gu
Keyword(s):  
State Space ◽  
Real Time ◽  
Real World ◽  
Heuristic Search ◽  
Time Pressure ◽  
Autonomous Systems ◽  
Optimal Solutions ◽  
Algorithmic Approach ◽  
Heuristic Search Methods ◽  
Computational Resources

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.

scholarly journals Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3371 ◽  
Author(s):  
Hossain ◽  
Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Moving Objects ◽  
State Of The Art ◽  
Target Position ◽  
Guidance System ◽  
Aerial Imagery ◽  
Detection And Tracking ◽  
Real Time Tracking

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

A Simplistic Approach for Lightweight Multi-Agent SLAM Algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 67-83
Author(s):  
Anton Filatov ◽  
Kirill Krinkin
Keyword(s):  
State Of The Art ◽  
Simultaneous Localization And Mapping ◽  
The State ◽  
Limited Resources ◽  
Challenging Problem ◽  
Multiple Agents ◽  
Localization And Mapping ◽  
Multi Agent ◽  
Slam Algorithm ◽  
Computational Resources

Limitation of computational resources is a challenging problem for moving agents that launch such algorithms as simultaneous localization and mapping (SLAM). To increase the accuracy on limited resources one may add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. In this article, the state-of-the-art in multi-agent SLAM algorithms is presented, and an approach that extends laser 2D single hypothesis SLAM for multiple agents is introduced. The article contains a description of problems that are faced in front of a developer of such approach including questions about map merging, relative pose calculation, and roles of agents.

scholarly journals Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid

2021 ◽  
Vol 9 (VI) ◽  
pp. 5452-5458
Author(s):  
Arpit Gupta
Keyword(s):  
Real Time ◽  
High Speed ◽  
Visual Detection ◽  
Autonomous Systems ◽  
Sliding Window ◽  
The Past ◽  
Detection And Tracking ◽  
Window Method ◽  
Sliding Window Method ◽  
State Of Art

Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and the tracking. The detection is based on the YOLOv3 (You Only Look Once v3) algorithm and a sliding window method. The tracking is based on the GOTURN (Generic Object Tracking Using Regression Networks) algorithm, which allows the tracking of generic objects at high speed. In order to allow autonomous tracking and enhance the accuracy, a combination of GOTURN and tracking by detection using YOLOv3 was developed.

scholarly journals A Micro SLAM System Based on ORB for RGB-D Cameras

2018 ◽  
Vol 160 ◽  
pp. 07001
Author(s):  
Fei Wang ◽  
Xiaogang Ruan ◽  
Pengfei Dong ◽  
OUATTARA SIE
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
State Of The Art ◽  
Estimation Error ◽  
Bundle Adjustment ◽  
Optimization Strategy ◽  
Localization And Mapping ◽  
Benchmark Datasets ◽  
Speed Up

In this paper, a micro SLAM system based on ORB features for RGB-D cameras has been proposed. With only a RGB-D sensor, this method can be applied in small environment for localization and mapping. Furthermore, the task of 3D reconstruction can also be accomplished by using the approach. The pose graph based on Bundle Adjustment is adopted for reducing the estimation error. In order to further speed up computing to meet the requirement of real-time, we have proposed the piecewise optimization strategy. The approach is evaluated on public benchmark datasets. Compared with several state-of-the-art scheme, this method has proven to work well in these environments.

scholarly journals A Wearable Navigation Device for Visually Impaired People Based on the Real-Time Semantic Visual SLAM System

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1536
Author(s):  
Zhuo Chen ◽  
Xiaoming Liu ◽  
Masaru Kojima ◽  
Qiang Huang ◽  
Tatsuo Arai
Keyword(s):  
Real Time ◽  
Visually Impaired ◽  
Semantic Information ◽  
Visual Slam ◽  
Control Center ◽  
Computing Platform ◽  
Surrounding Environment ◽  
Visually Impaired People ◽  
Localization And Mapping ◽  
Impaired People

Wearable auxiliary devices for visually impaired people are highly attractive research topics. Although many proposed wearable navigation devices can assist visually impaired people in obstacle avoidance and navigation, these devices cannot feedback detailed information about the obstacles or help the visually impaired understand the environment. In this paper, we proposed a wearable navigation device for the visually impaired by integrating the semantic visual SLAM (Simultaneous Localization And Mapping) and the newly launched powerful mobile computing platform. This system uses an Image-Depth (RGB-D) camera based on structured light as the sensor, as the control center. We also focused on the technology that combines SLAM technology with the extraction of semantic information from the environment. It ensures that the computing platform understands the surrounding environment in real-time and can feed it back to the visually impaired in the form of voice broadcast. Finally, we tested the performance of the proposed semantic visual SLAM system on this device. The results indicate that the system can run in real-time on a wearable navigation device with sufficient accuracy.

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