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With the evolution of human–robot collaboration in advanced manufacturing, multisensor integration has increasingly become a critical component for ensuring safety during human–robot interactions. Given the disparities in range scales, densities, ...

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• A method for sensor fusion in human–robot environment is proposed.
• A coarse localization algorithm is proposed to address point cloud scale differences.
• An improved FPFH algorithm based on overlap ratio for coarse registration.

The York University Teledyne Optech (YUTO) Mobile Mapping System (MMS) Dataset, encompassing four sequences totaling 20.1 km, was thoroughly assembled through two data collection expeditions on August 12, 2020, and June 21, 2019. Acquisitions were ...

The emergence and widespread usage of mobile devices have revolutionized the field of Augmented Reality (AR), allowing the technology to grow rapidly, and attracting significant attention from different businesses such as e-commerce, healthcare, ...

LiDAR super-resolution can improve the quality of point cloud data, which is critical for improving many downstream tasks such as object detection, identification, and tracking. Traditional LiDAR super-resolution models often struggle with issues ...

LiDAR Simultaneous Localization and Mapping (SLAM) plays a crucial role in intelligent robotics, finding extensive applications in autonomous driving and exploration. The traditional feature-based LiDAR SLAM holds a prominent position due to its ...

LiDAR technology holds vast potential across various sectors, including robotics, autonomous driving, and urban planning. However, the performance of current LiDAR sensors is hindered by limited field of view (FOV), low resolution, and lack of flexible ...

This paper proposes a novel NeRF (Neural Radiance Fields), called WF-NeRF, for accurately recovering wavefront signals with multiple depth values. Although range sensors such as LiDARs typically return depth as scalars, the proposed method uses 1D ...

Adding LiDAR for an autonomous driving system will complement the weaknesses of a camera-only solution and enhance its robustness. To fully exploit the multimodal advantage, existing works have proposed various multimodal fusion algorithms to ...

Advancements in Earth observation technologies have greatly enhanced the potential of integrating hyperspectral (HS) images with Light Detection and Ranging (LiDAR) data for land use and land cover classification. Despite this, most existing ...

• A newly Cross-modal feature aggregation is introduced for HS and LiDAR.
• Two feature aggregation strategies refine spatial location details.
• INNs feature enhancement is to preserve local contextual integrity.

Point cloud data from light detection and ranging (LiDAR) is often used for its spatial qualities, particularly in smart city projects involving vehicles and pedestrians. In this paper, the authors introduce a streaming and an on-demand pipeline for ...

Loop closure detection, also known as place recognition, a key component of simultaneous localization and mapping (SLAM) systems, aims to recognize previously visited locations and reduce the accumulated drift error caused by odometry. Current ...

Collaborative Vehicular Perception (CVP) enables connected and autonomous vehicles (CAVs) to cooperatively extend their views through wirelessly sharing their sensor data. Existing CVP systems employ either a vehicle-to-vehicle (V2V) or vehicle-to-...

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• Mechanically harvesting strawberries is challenging due to the labor intensity and susceptibility of the fruit to mechanical damage.
• A comprehensive review of recent progress in mechanical harvesting of fresh market strawberries was ...

This article reviews the research and development progress of mechanical harvesting technologies for fresh market strawberries in the past few decades, focusing on the main technologies such as cut-and-separate method, harvest-assist method, ...

3D object detection in LiDAR point clouds is crucial for computer vision tasks such as autonomous driving. The two-stage approach with point cloud completion achieves remarkable performance by generating semantic surface points for foreground ...

• Point cloud boundary information is complemented without the need for introduction additional data.
• A dual channel selection module is designed to fuse RoI grid point and RoI boundary point features.
• Using low-rank tensors to ...

LiDAR point clouds can effectively depict the motion and posture of objects in three-dimensional space. Many studies accomplish the 3D object detection by voxelizing point clouds. However, in autonomous driving scenarios, the sparsity and ...

Mapping, assessing, and creating personalized routes of indoor spaces for people with disabilities remains a grand challenge in accessibility research. Drawing on recent work in robotics as well as emergent work in smartphone-based mapping, we introduce ...

We present LiLoc, a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. LiLoc stands out with two key differentiators. First, unlike traditional localization ...

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Light Detection And Ranging (LiDAR) sensor offers solutions to extract real-time information of vehicle’s surroundings and can provide a new opportunity to improve the road safety related issues in mixed traffic conditions. This paper explores ...

3D sensing is a fundamental task for Autonomous Vehicles. Its deployment often relies on aligned RGB cameras and LiDAR. Despite meticulous synchronization and calibration, systematic misalignment persists in LiDAR projected depthmap. This is due ...$\mathrm{}$

LiDAR-based 3D object detection models show remarkable performance, however their effectiveness diminishes in adverse weather. On the other hand, 4D radar exhibits strengths in adverse weather but faces limitations in standalone use. While fusing ...