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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 ...

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-...

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 ...

Popular representation learning methods encourage feature invariance under transformations applied at the input. However, in 3D perception tasks like object localization and segmentation, outputs are naturally equivariant to some transformations, ...

3D object detection is one of the most important components in any Self-Driving stack, but current state-of-the-art (SOTA) lidar object detectors require costly & slow manual annotation of 3D bounding boxes to perform well. Recently, several ...

The fusion of LiDAR and camera sensors is extensively applied in the field of 3D reconstruction. LiDAR provides high-accuracy measurements and strong anti-interference capabilities, while cameras offer rich color and texture information. This paper ...

3D point neural network (PNN) segmentation using LiDAR data has emerged as a fundamental stage of high-level intelligence algorithms for autonomous applications such as SLAM, path planning, object detection, etc. However, previous processors were not ...

3-D mapping of buildings is crucial for urban renewal, but traditional LiDAR-based mapping methods are often less effective for buildings with narrow spaces and limited geometric features. Current methods attempt to overcome this by integrating ...

Multimodal 3D object detection has gained significant attention due to the fusion of light detection and range (LiDAR) and RGB data. Existing 3D detection models in autonomous driving are typically trained on dense point cloud data from high-...

Accurate and reliable perception is essential for the safe operation of autonomous cars. The task of Light Detection And Ranging (LiDAR) panoptic segmentation plays a crucial role in predicting point-level classes and instance IDs within a 3D ...

Unlike the earlier 3D object detection approaches that formulate hand-crafted dense (in thousands) object proposals by leveraging anchors on dense feature maps, we formulate n p (in hundreds) number of learnable sparse object proposals to predict ...

In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (...