This paper proposes a real-time, versatile Simultaneous Localization and Mapping (SLAM) and object localization system, which fuses measurements from LiDAR, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). Our system can...
Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping...
Vision-based localization techniques and detection technologies are key algorithms for the localization and navigation of unmanned vehicles. Especially in scenarios where GPS signals are missing, Simultaneous Localization and Mapping (SLAM) technique...
Simultaneous Localization and Mapping (SLAM) systems face high computational demands, hindering their real-time implementation on low-end computers. An approach to addressing this challenge involves offline processing, i.e., a map of the environment...
Accurate and robust simultaneous localization and mapping (SLAM) systems are crucial for autonomous underwater vehicles (AUVs) to perform missions in unknown environments. However, directly applying deep learning-based SLAM methods to underwater envi...
A smoothing localization method for Global Navigation Satellite System (GNSS) and visual Simultaneous Localization and Mapping (SLAM) system is proposed to identify GNSS spoofing, optimize the cumulative error of the GNSS/visual SLAM system, and obta...
Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The S...
Existing Simultaneous Localization and Mapping (SLAM) algorithms provide precise local pose estimation and real-time scene reconstruction, widely applied in autonomous navigation for land vehicles. However, the odometry of SLAM algorithms exhibits lo...
Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy...
LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization...
This work presents a robust visual localization technique based on an omnidirectional monocular sensor for mobile robotics applications. We intend to overcome the non-linearities and instabilities that the camera projection systems typically introduc...
Recently, the map matching-assisted positioning method based on micro-electromechanical systems (MEMS) inertial devices has become a research hotspot for indoor pedestrian positioning; however, these are based on existing indoor electronic maps. In t...
Scale ambiguity and drift are inherent drawbacks of a pure-visual monocular simultaneous localization and mapping (SLAM) system. This problem could be a crucial challenge for other robots with range sensors to perform localization in a map previously...
Due to various typical unstructured factors in the environment of photovoltaic power stations, such as high feature similarity, weak textures, and simple structures, the motion model of the ORB-SLAM2 algorithm performs poorly, leading to a decline in...
In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and...
The method of simultaneous localization and mapping (SLAM) using a light detection and ranging (LiDAR) sensor is commonly adopted for robot navigation. However, consumer robots are price sensitive and often have to use low-cost sensors. Due to the po...
This work reports an information-based landmarks assisted simultaneous localization and mapping (InfoLa-SLAM) in large-scale scenes using single-line lidar. The solution employed two novel designs. The first design was a keyframe selection method bas...
Mobile olfaction is one of the applications of mobile robots. Metal oxide sensors (MOX) are mobile robots’ most popular gas sensors. However, the sensor has drawbacks, such as high-power consumption, high operating temperature, and long recover...
Reliable navigation for mobile robots in dynamic, human-populated environments remains a significant challenge, as moving objects often cause localization drift and map corruption. While Simultaneous Localization and Mapping (SLAM) techniques excel i...
In order to attain precise and robust transformation estimation in simultaneous localization and mapping (SLAM) tasks, the integration of multiple sensors has demonstrated effectiveness and significant potential in robotics applications. Our work eme...
This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of th...
This paper discusses a novel embedded system-on-chip 3D localization and mapping (eSoC-LAM) implementation, that followed a co-design approach with the primary aim of being deployed in a small system on a programmable chip (SoPC), the Intel’s (a.k.a...
The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, S...
We evaluated a new wearable technology that fuses inertial sensors and cameras for tracking human kinematics. These devices use on-board simultaneous localization and mapping (SLAM) algorithms to localize the camera within the environment. Significan...
RTOB-SLAM is a new low-computation framework for real-time onboard simultaneous localization and mapping (SLAM) and obstacle avoidance for autonomous vehicles. A low-resolution 2D laser scanner is used and a small form-factor computer perform all com...
Most visual simultaneous localization and mapping (SLAM) systems are based on the assumption of a static environment in autonomous vehicles. However, when dynamic objects, particularly vehicles, occupy a large portion of the image, the localization a...
In this work, a comparative study between an Ultra Wide-Band (UWB) localization system and a Simultaneous Localization and Mapping (SLAM) algorithm is presented. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy...
Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localizat...
This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementat...
Trajectory estimation from stereo image sequences remains a fundamental challenge in Visual Simultaneous Localization and Mapping (V-SLAM). To address this, we propose a novel approach that focuses on the identification and matching of keypoints with...
When facing geometrically similar environments such as subway tunnels, Scan-Map registration is highly dependent on the correct initial value of the pose, otherwise mismatching is prone to occur, which limits the application of SLAM (Simultaneous Loc...
Simultaneous Localization and Mapping (SLAM) is one of the key technologies with which to address the autonomous navigation of mobile robots, utilizing environmental features to determine a robot’s position and create a map of its surroundings....
This study proposes a tightly coupled multi-sensor Simultaneous Localization and Mapping (SLAM) framework that integrates RGB-D and inertial measurements to achieve highly accurate 6 degree of freedom (6DOF) metric localization in a variety of enviro...
This paper presents a performance analysis of two open-source, laser scanner-based Simultaneous Localization and Mapping (SLAM) techniques (i.e., Gmapping and Hector SLAM) using a Microsoft Kinect to replace the laser sensor. Furthermore, the paper...
Mini-drones can be used for a variety of tasks, ranging from weather monitoring to package delivery, search and rescue, and also recreation. In outdoor scenarios, they leverage Global Positioning Systems (GPS) and/or similar systems for localization...
Collaborative simultaneous localization and mapping have a great impact on various applications such as search-and-rescue and agriculture. For each agent, the key to performing collaboration is to measure the motion relative to other participants or...
To address the issue of agricultural robot loss of control caused by GNSS signal degradation or loss in complex agricultural environments such as farmland and orchards, this study proposes a neural network-based SLAM/GNSS fusion localization algorith...
Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous loc...
Multi-sensor fusion is pivotal in augmenting the robustness and precision of simultaneous localization and mapping (SLAM) systems. The LiDAR–visual–inertial approach has been empirically shown to adeptly amalgamate the benefits of these s...
Lidar SLAM (simultaneous localization and mapping) systems provide vehicles with high-precision maps and localization for environmental perception. However, sensor noise and dynamic changes can lead to the localization drift or localization failure o...
Current research on SLAM can be divided into two parts according to the research scenario: SLAM research in dynamic scenarios and SLAM research in static scenarios. Research is now relatively well established for static environments. However, in dyna...
Aiming to address the problems of scale uncertainty and dynamic object interference in monocular visual simultaneous localization and mapping (SLAM), this paper proposes an absolute depth estimation network-based monocular visual SLAM method, namely,...
This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtain...
One of the main challenges of aerial robots navigation in indoor or GPS-denied environments is position estimation using only the available onboard sensors. This paper presents a Simultaneous Localization and Mapping (SLAM) system that remotely calcu...
This paper accompanies the initial public release of the EDI multi-modal SLAM dataset, a collection of long tracks recorded with a portable sensor package. These include two global shutter RGB camera feeds, LiDAR scans, as well as inertial and GNSS d...
Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indo...
A cooperative dynamic range-only simultaneous localization and mapping (CDRO-SLAM) algorithm based on the sum of Gaussian (SoG) filter was recently introduced. The main characteristics of the CDRO-SLAM are (i) the integration of inter-node ranges as...
This article presents a new sensor fusion method for visual simultaneous localization and mapping (SLAM) through integration of a monocular camera and a 1D-laser range finder. Such as a fusion method provides the scale estimation and drift correction...
Visual Simultaneous Localization and Mapping (VSLAM) is significant in unmanned driving, being is used to locate vehicles and create environmental maps, and provides a basis for navigation and decision making. However, in inevitable dark night enviro...
Agricultural robots can mitigate labor shortages and advance precision farming. However, the dense vegetation canopies and uneven terrain in orchard environments reduce the reliability of traditional GPS-based localization, thereby reducing navigatio...
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