SLAM (Simultaneous Localization and Mapping) is mainly composed of five parts: sensor data reading, front-end visual odometry, back-end optimization, loopback detection, and map building. And when visual SLAM is estimated by visual odometry only, cum...
Back-end optimization is a key process to eliminate the cumulative error in Visual Simultaneous Localization and Mapping (VSLAM). Existing VSLAM frameworks often use kernel function-based back-end optimization methods. However, these methods typicall...
Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, t...
Simultaneous Localization and Mapping (SLAM) enables real-time six-degree-of-freedom (6-DOF) state estimation and spatial reconstruction in environments where global navigation satellite systems (GNSSs) are unavailable or unreliable. Light Detection...
To address accuracy and reliability challenges in simultaneous localization and mapping (SLAM) systems under extreme conditions, this paper presents LIVE-SLAM, a tightly-coupled LiDAR–inertial–visual framework. The technical core integrat...
As the electrification of the automotive industry accelerates, the importance of small-scale motors used in applications such as HVAC systems and water pumps is growing. To design small motors that exhibit high efficiency and high output within limit...
Coal-fired power plants equipped with selective catalytic reduction (SCR) and wet flue-gas desulfurization (WFGD) can co-control mercury, but performance is limited by incomplete upstream Hg0 oxidation and downstream re-emission. This study evaluated...
LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detect...
With the rapid expansion of the Electric Sport Utility Vehicle (ESUV) market, capturing consumer aesthetic preferences and emotional needs through front-end styling has become a key issue in automotive design. However, traditional Kansei Engineering...
In radio frequency (RF) systems, the mixer is a critical component for achieving frequency conversion in electromagnetic sensor backends. This paper proposes a mixer design methodology aimed at improving noise figure and conversion gain specifically...
This paper proposes a novel two-stage phase-shift optimization strategy to reduce DC bus ripple current in a two-phase cascaded boost converter. Conventional 180° interleaving causes frequency mismatch between the front-end and back-end stages, d...
Over the past few years, the computation capability of field-programmable gate arrays (FPGAs) has increased tremendously. This has led to the increase in the complexity of the designs implemented on FPGAs and to the time taken by the FPGA back-end fl...
To address the challenges in multi-robot collaborative SLAM, including excessive redundant computations and low processing efficiency in candidate loop closure selection during front-end loop detection, as well as high computational complexity and lo...
High-precision localization is essential for autonomous navigation and environment perception of unmanned aerial vehicles (UAVs) in complex power inspection scenarios. To overcome the limited accuracy and accumulated drift of conventional GPS-based s...
Robustness against background noise and reverberation is essential for many real-world speech-based applications. One way to achieve this robustness is to employ a speech enhancement front-end that, independently of the back-end, removes the environm...
Reliable autonomous navigation for Tianwen-3-class Mars rotorcraft must satisfy both sampling-level accuracy and hard real-time execution under severe onboard computational constraints. To address this challenge, we develop MarsBird-VII, a mission-co...
Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. I...
State estimation and mapping based on Light Detection and Ranging (LIDAR) are important for autonomous systems. Point cloud registration is a crucial module affecting the accuracy and real-time performance of LIDAR simultaneous localization and mappi...
In this paper, we present a novel red-green-blue-depth simultaneous localization and mapping (RGB-D SLAM) algorithm based on cloud robotics, which combines RGB-D SLAM with the cloud robot and offloads the back-end process of the RGB-D SLAM algorithm...
Owing to the low cost, small size, and convenience for installation, 2D LiDAR has been widely used in mobile robots for simultaneous positioning and mapping (SLAM). However, traditional 2D LiDAR SLAM methods have low robustness and accuracy in LiDAR-...
As we increasingly use advanced technology nodes to design integrated circuits (ICs), physical designers and electronic design automation (EDA) providers are facing multiple challenges, firstly, to honor all physical constraints coming with cutting-e...
We present an efficient incremental SLAM back-end that reduces computation while preserving accuracy close to that of a full incremental Gauss–Newton (GN) solver across benchmark pose-graph datasets. The method combines information-guided gatin...
Nonlinear optimization is a central component of visual odometry and simultaneous localization and mapping (SLAM), but its repeated small- and medium-scale linear algebra operations are difficult to deploy efficiently on embedded hardware. This paper...
In this paper, a new two-step joint optimization approach based on the asynchronous subregion optimization method is proposed for training a pipeline model composed of two different models. The first-step processing of the proposed joint optimization...
Increasing costs of diagnostic healthcare have necessitated the development of hardware independent non-invasive Point of Care (POC) systems. Although anemia prevalence rates in global populations vary between 10% and 60% in various demographic group...
Pedestrian detection is an important research topic in the field of visual cognition and autonomous driving systems. The proposal of the YOLO model has significantly improved the speed and accuracy of detection. To achieve full day detection performa...
Generally, the key issues of 2D LiDAR-based simultaneous localization and mapping (SLAM) for indoor application include data association (DA) and closed-loop detection. Particularly, a low-texture environment, which refers to no obvious changes betwe...
Autonomous navigation of unmanned aerial vehicles (UAVs) in unknown complex environments requires safe, fast and efficient path planning algorithms. Currently, the two-stage framework of “front-end search and back-end optimization” is wid...
LiDAR and cameras, often regarded as the “eyes” of intelligent driving vehicles, are vulnerable to adverse weather conditions like haze, rain, and snow, compromising driving safety. In order to solve this problem and enhance the environme...
In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and ac...
With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surroundi...
Diamond dicing blades are profound cutting tools that find their applications in semiconductor back-end packaging and assembly processes. To fully appreciate the benefits of the dicing blade technique for precision machining, a deeper understanding i...
Implementing cryptographic primitives on resource-constrained IoT devices involves tight latency, code-size, and energy budgets. This work proposes a general LLVM backend instruction-selection strategy that recognizes single-bit update idioms—t...
The Korean government is currently evaluating two alternatives, direct disposal and pyroprocessing, for the disposal of spent nuclear fuel. This paper presents the ranking results of comparing and evaluating direct disposal and pyro-SFR fuel cycle al...
Surface-checking is a significant quality issue of veneer and sliced lamellae-based wood products. This study explores how surface-checking in sliced lamellae-based engineered wood Flooring (EWF) is influenced by two key structure parameters: core ty...
Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process...
To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating env...
To improve localization and pose precision of visual–inertial simultaneous localization and mapping (viSLAM) in complex scenarios, it is necessary to tune the weights of the visual and inertial inputs during sensor fusion. To this end, we propo...
In this paper, we proposed Mix-VIO, a monocular and binocular visual-inertial odometry, to address the issue where conventional visual front-end tracking often fails under dynamic lighting and image blur conditions. Mix-VIO adopts a hybrid tracking a...
Complex-product manufacturing workshops are characterized by diverse process routes, heterogeneous machine capabilities, tight coupling between processing and assembly, and stringent parent–child kitting constraints. These characteristics make...
A tightly coupled hybrid monocular visual SLAM system for unmanned underwater vehicles (UUVs) is introduced in this paper. Specifically, we propose a robust three-step hybrid tracking strategy. The feature-based method initially provides a rough pose...
The localization performance of visual–inertial simultaneous localization and mapping (VI-SLAM) strongly depends on front-end feature matching. In degraded scenes with low illumination, repetitive textures, and weak textures, traditional geomet...
This paper presents a real-time, robust and low-drift depth-only SLAM (simultaneous localization and mapping) method for depth cameras by utilizing both dense range flow and sparse geometry features from sequential depth images. The proposed method i...
Trading off the allocation of limited computational resources between front-end path generation and back-end trajectory optimization plays a key role in improving the efficiency of unmanned aerial vehicle (UAV) motion planning. In this paper, a sampl...
For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve t...
The LinoSPAD camera system is a modular, compact and versatile time-resolved camera system, combining a linear 256 high fill factor pixel CMOS SPAD (single-photon avalanche diode) sensor with an FPGA (field-programmable gate array) and USB 3.0 transc...
Simultaneous Localization and Mapping (SLAM) systems require accurate and globally consistent mapping to ensure the long-term stable operation of robots or vehicles. However, for the commercial applications of indoor sweeping robots, the system needs...
Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expens...
Simultaneous localization and mapping (SLAM) is one of the key technologies for mobile robots to achieve autonomous driving, and the lidar SLAM algorithm is the mainstream research scheme. Firstly, this paper introduces the overall framework of lidar...
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...
of 8