Search Results for author:
Found 26 papers, 15 papers with code
GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction
The accurate LiDAR point clouds enable a trained neural signed distance field to offer a manifold geometry field, This motivates us to offer an SDF-based Gaussian initialization for physically grounded primitive placement and a comprehensive geometric regularization for geometrically consistent rendering and reconstruction.
Temporal Overlapping Prediction: A Self-supervised Pre-training Method for LiDAR Moving Object Segmentation
Moving object segmentation (MOS) on LiDAR point clouds is crucial for autonomous systems like self-driving vehicles.
Diffusion Model for Interest Refinement in Multi-Interest Recommendation
Multi-interest candidate matching plays a pivotal role in personalized recommender systems, as it captures diverse user interests from their historical behaviors.
GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping
Our map system comprises a global Gaussian map and a sliding window of Gaussians, along with an IESKF-based odometry.
Multivariate Time Series Anomaly Detection using DiffGAN Model
In recent years, some researchers have applied diffusion models to multivariate time series anomaly detection.
Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs
Entity alignment (EA) refers to the task of linking entities in different knowledge graphs (KGs).
Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems
We unify the training of the NDF and NeRF using a spatial-varying scale SDF-to-density transformation for levels of detail for both structure and appearance.
FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry
The fusion of both visual and LiDAR measurements is based on a single unified voxel map where the LiDAR module constructs the geometric structure for registering new LiDAR scans and the visual module attaches image patches to the LiDAR points.
Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations
This paper presents and evaluates a global visual localization system capable of localizing a single camera image across various 3D map representations built using both visual and lidar sensing.
NALA: an Effective and Interpretable Entity Alignment Method
In this paper, we introduce NALA, an entity alignment method that captures three types of logical inference paths with Non-Axiomatic Logic (NAL).
An Efficient Plane Extraction Approach for Bundle Adjustment on LiDAR Point clouds
However, the accuracy and speed of LiDAR bundle adjustment depend on the quality of plane extraction, which provides point association for LiDAR BA.
ImMesh: An Immediate LiDAR Localization and Meshing Framework
This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets.
STD: Stable Triangle Descriptor for 3D place recognition
For a triangle, its shape is uniquely determined by the length of the sides or included angles.
R$^3$LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator
The LIO subsystem utilizes the measurements from a LiDAR for reconstructing the geometric structure (i. e., the positions of 3D points), while the VIO subsystem simultaneously recovers the radiance information of the geometric structure from the input images.
R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
Moreover, to make R3LIVE more extensible, we develop a series of offline utilities for reconstructing and texturing meshes, which further minimizes the gap between R3LIVE and various of 3D applications such as simulators, video games and etc (see our demos video).
R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping
Our proposed framework is composed of two parts: the filter-based odometry and factor graph optimization.
Robotics
BALM: Bundle Adjustment for Lidar Mapping
We propose a framework for bundle adjustment (BA) on sparse lidar points and incorporate it to a lidar odometry and mapping (LOAM) to lower the drift.
Robotics
FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
To lower the computation load in the presence of large number of measurements, we present a new formula to compute the Kalman gain.
Robotics
Robots State Estimation and Observability Analysis Based on Statistical Motion Models
It is shown that this new paradigm is much simpler and more natural than existing methods based on quaternion parameterizations.
A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs
To improve the system robustness and performance in self-localization and mapping, we develop a decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs.
Robotics
Consolidating Commonsense Knowledge
Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities.
A fast, complete, point cloud based loop closure for LiDAR odometry and mapping
This paper presents a loop closure method to correct the long-term drift in LiDAR odometry and mapping (LOAM).
Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV
LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment.
Acceleration Based Iterative Learning Control for Pugachev's Cobra Maneuver with Quadrotor Tail-sitter VTOL UAVs
Both the feedback controller and the iterative learning feed-forward controller are based on the aircraft acceleration model, which is directly measurable by the onboard accelerometer.
Systems and Control
Flying through a narrow gap using neural network: an end-to-end planning and control approach
To this end, we propose an end-to-end policy network, which imitates from the traditional pipeline and is fine-tuned using reinforcement learning.
Robotics
Detecting facial landmarks in the video based on a hybrid framework
Finally, the facial landmarks in the current frame are exactly detected from the validated face bounding box via the landmark detector.
