scholarly journals Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 41
Author(s):  
Sander Krul ◽  
Christos Pantos ◽  
Mihai Frangulea ◽  
João Valente
Keyword(s):  
Precision Agriculture ◽  
Dairy Farm ◽  
Physical Space ◽  
Indoor Environments ◽  
Localization Problem ◽  
Time Data ◽  
Livestock Management ◽  
Monocular Camera ◽  
Localization And Mapping ◽  
Waypoint Navigation

Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology.

Localization and Mapping for Indoor Navigation

2020 ◽  
pp. 930-954 ◽  
Author(s):  
Heba Gaber ◽  
Mohamed Marey ◽  
Safaa Amin ◽  
Mohamed F. Tolba
Keyword(s):  
System Architecture ◽  
Large Range ◽  
Simultaneous Localization And Mapping ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Challenging Problem ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Robotic Missions

Mapping and exploration for the purpose of navigation in unknown or partially unknown environments is a challenging problem, especially in indoor environments where GPS signals can't give the required accuracy. This chapter discusses the main aspects for designing a Simultaneous Localization and Mapping (SLAM) system architecture with the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. Achieving this capability makes these systems significantly more autonomous and ideal for a large range of applications, especially indoor navigation for humans and for robotic missions. This chapter surveys the existing algorithms and technologies used for localization and mapping and highlights on using SLAM algorithms for indoor navigation. Also the proposed approach for the current research is presented.

Interval arithmetic to support effective indoor positioning of software agents

2020 ◽  
Vol 14 (1) ◽  
pp. 59-73
Author(s):  
Stefania Monica ◽  
Federico Bergenti
Keyword(s):  
Mobile Device ◽  
Interval Arithmetic ◽  
Indoor Environment ◽  
Optimization Problem ◽  
Sufficient Accuracy ◽  
Location Based Services ◽  
Geometric Algorithms ◽  
Line Of Sight ◽  
Indoor Environments ◽  
Localization Problem

 The provision of advanced location-based services in indoor environments is based on the possibility of estimating the positions of mobile devices with sufficient accuracy and robustness. An algorithm to allow a software agent hosted on a mobile device to estimate the position of its device in a known indoor environment is proposed under the ordinary assumption that fixed beacons are installed in the environment at known locations. Rather than making use of geometric considerations to estimate the position of the device, the proposed algorithm first transforms the localization problem into a related optimization problem, which is then solved by means of interval arithmetic to provide the agent with accurate and robust position estimates. The adopted approach solves a major problem that severely limits the accuracy of the position estimates that ordinary geometric algorithms provide when the beacons are positioned to maximize line-of-sight coverage. Experimental results confirm that the proposed algorithm provides position estimates that are independent of the positions of the beacons, and they show that the algorithm outperforms a well-known geometric algorithm.

Automatically Annotated Mapping for Indoor Mobile Robot Applications

2012 ◽  
Author(s):  
Ali Gürcan Özkil ◽  
Thomas Howard
Keyword(s):  
Mobile Robot ◽  
Practical Method ◽  
Service Robots ◽  
Service Robot ◽  
Mobile Service ◽  
Indoor Environments ◽  
And Topology ◽  
Localization And Mapping ◽  
Indoor Mobile Robot ◽  
Mobile Service Robots

This paper presents a new and practical method for mapping and annotating indoor environments for mobile robot use. The method makes use of 2D occupancy grid maps for metric representation, and topology maps to indicate the connectivity of the ‘places-of-interests’ in the environment. Novel use of 2D visual tags allows encoding information physically at places-of-interest. Moreover, using physical characteristics of the visual tags (i.e. paper size) is exploited to recover relative poses of the tags in the environment using a simple camera. This method extends tag encoding to simultaneous localization and mapping in topology space, and fuses camera and robot pose estimations to build an automatically annotated global topo-metric map. It is developed as a framework for a hospital service robot and tested in a real hospital. Experiments show that the method is capable of producing globally consistent, automatically annotated hybrid metric-topological maps that is needed by mobile service robots.

scholarly journals An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1742 ◽  
Author(s):  
Chuang Qian ◽  
Hongjuan Zhang ◽  
Jian Tang ◽  
Bijun Li ◽  
Hui Liu
Keyword(s):  
Laser Scanner ◽  
Location Based Services ◽  
Experimental Result ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization And Mapping ◽  
Map Generation ◽  
Representation Method ◽  
Rigid Body Transformation ◽  
Grid Based

An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.

scholarly journals Nature-Inspired Drone Swarming for Real-Time Aerial Data-Collection Under Dynamic Operational Constraints

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 71 ◽  
Author(s):  
Hanno Hildmann ◽  
Ernö Kovacs ◽  
Fabrice Saffre ◽  
A. F. Isakovic
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Precision Agriculture ◽  
Computational Cost ◽  
Civil Defense ◽  
Time Data ◽  
Communication Infrastructure ◽  
Distributed Approach ◽  
Nature Inspired Algorithm ◽  
Real Time Data

Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation.

scholarly journals Monocular Vision SLAM for Indoor Aerial Vehicles

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Koray Çelik ◽  
Arun K. Somani
Keyword(s):  
Indoor Navigation ◽  
Monocular Vision ◽  
Indoor Environments ◽  
Feature Points ◽  
Monocular Camera ◽  
Aerial Vehicles ◽  
Navigation Strategy ◽  
Aerial Vehicle ◽  
Technological Challenge ◽  
Limited Sensing

This paper presents a novel indoor navigation and ranging strategy via monocular camera. By exploiting the architectural orthogonality of the indoor environments, we introduce a new method to estimate range and vehicle states from a monocular camera for vision-based SLAM. The navigation strategy assumes an indoor or indoor-like manmade environment whose layout is previously unknown, GPS-denied, representable via energy based feature points, and straight architectural lines. We experimentally validate the proposed algorithms on a fully self-contained microaerial vehicle (MAV) with sophisticated on-board image processing and SLAM capabilities. Building and enabling such a small aerial vehicle to fly in tight corridors is a significant technological challenge, especially in the absence of GPS signals and with limited sensing options. Experimental results show that the system is only limited by the capabilities of the camera and environmental entropy.

scholarly journals GEOFENCING-BASED LOCALIZATION FOR 3D DATA ACQUISITION NAVIGATION

2016 ◽  
Vol XLI-B4 ◽  
pp. 319-324
Author(s):  
M. Nakagawa ◽  
T. Kamio ◽  
H. Yasojima ◽  
T. Kobayashi
Keyword(s):  
Data Acquisition ◽  
Indoor Positioning ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Positioning Systems ◽  
3D Data ◽  
Localization And Mapping ◽  
Sensor Position ◽  
Position Data ◽  
Infrastructure Inspection

Users require navigation for many location-based applications using moving sensors, such as autonomous robot control, mapping route navigation and mobile infrastructure inspection. In indoor environments, indoor positioning systems using GNSSs can provide seamless indoor-outdoor positioning and navigation services. However, instabilities in sensor position data acquisition remain, because the indoor environment is more complex than the outdoor environment. On the other hand, simultaneous localization and mapping processing is better than indoor positioning for measurement accuracy and sensor cost. However, it is not easy to estimate position data from a single viewpoint directly. Based on these technical issues, we focus on geofencing techniques to improve position data acquisition. In this research, we propose a methodology to estimate more stable position or location data using unstable position data based on geofencing in indoor environments. We verify our methodology through experiments in indoor environments.

scholarly journals IMPROVED REFERENCE KEY FRAME ALGORITHM

2019 ◽  
Vol IV-2/W5 ◽  
pp. 133-139 ◽  
Author(s):  
H. A. Mohamed ◽  
A. Moussa ◽  
M. M. Elhabiby ◽  
N. El-Sheimy
Keyword(s):  
Autonomous Vehicles ◽  
Previous History ◽  
External Source ◽  
Indoor Environments ◽  
Scan Matching ◽  
Detection Range ◽  
Key Frame ◽  
Matching Process ◽  
Map Construction ◽  
Localization And Mapping

<p><strong>Abstract.</strong> The autonomous vehicles, such as wheeled robots and drones, efficiently contribute in the search and rescue operations. Specially for indoor environments, these autonomous vehicles rely on simultaneous localization and mapping approach (SLAM) to construct a map for the unknown environment and simultaneously to estimate the vehicle’s position inside this map. The result of the scan matching process, which is a key step in many of SLAM approaches, has a fundamental role of the accuracy of the map construction. Typically, local and global scan matching approaches, that utilize laser scan rangefinder, suffer from accumulated errors as both approaches are sensitive to previous history. The reference key frame (RKF) algorithm reduces errors accumulation as it decreases the dependency on the accuracy of the previous history. However, the RKF algorithm still suffers; as most of the SLAM approaches, from scale shrinking problem during scanning corridors that exceed the maximum detection range of the laser scan rangefinder. The shrinking in long corridors comes from the unsuccessful estimation of the longitudinal movement from the implemented RKF algorithm and the unavailability of this information from external source as well. This paper proposes an improvement for the RKF algorithm. This is achieved by integrating the outcomes of the optical flow with the RKF algorithm using extended Kalman filter (EKF) to overcome the shrinking problem. The performance of the proposed algorithm is compared with the RKF, iterative closest point (ICP), and Hector SLAM in corridors that exceed the maximum detection range of the laser scan rangefinder.</p>

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