35 accepted papers
LiDAR-based odometry is widely used in ground robot localization. However, current methods encounter challenges in accuracy and robustness due to structural degradation, system observational error, and accumulated error. To address the above issues, we propose CPBA-LIWO, a continuous-time LiDAR-Iner…
Abstract— Cross-View Geo-Localization (CVGL) localizes a query image via retrieval from georeferenced satellite imagery,yet severe viewpoint variation remains a central challenge. Recent advances often rely on heavy backbones or add-on modules that achieve high accuracy but are impractical on resour…
Adaptive Compressive Sensing (ACS) has attracted increasing attention for its ability to progressively improve image reconstruction quality by dynamically adjusting sampling allocation. Multi-stage sampling is a promising strategy that leverages intermediate reconstructions to guide sampling without…
Cross-modal Visual Geo-localization often aims to retrieve a satellite visible-light image of the same geographic lo cation from a large-scale database using an infrared image cap tured by an unmanned aerial vehicle (UAV), thereby achieving precise localization. This capability is crucial for autono…
The task of UAV-view geo-localization is to match a query image with database images to estimate the current geographic location of the query image. This is particularly useful in environments where GPS is not available or when the device fails. Although deep learning methods make sufficient progres
Visual Inertial Odometry (VIO) estimates predicted trajectories through self motion. With the popularization of artificial intelligence, deep learning-based VIO methods have shown better performance than traditional geometry-based VIO methods. However, in deep learning methods, how to better achieve
Cross-view geo-localization plays a critical role in Unmanned Aerial Vehicle (UAV) localization and navigation. However, significant challenges arise from the drastic viewpoint differences and appearance variations between images. Existing methods predominantly rely on semantic features from RGB ima
Recent advancements in Visual Language Models (VLMs) have significantly driven research in open-vocabulary 3D scene reconstruction, showcasing strong potential in open-set retrieval and semantic understanding. However, existing approaches face challenges in open-world environments: they either suffe
LiDAR point clouds 3D semantic segmentation enables efficient and accurate environmental sensing for intelligent vehicles and autonomous robots, greatly advancing these domains. Existing advanced methods that use 3D sparse convolutional often suffer from a small Effective Receptive Field (ERF), whic
Most current LiDAR-based odometry methods use point-to-local plane registration to constrain poses, ignoring the explicit plane structure in the environment. Due to noise interference and uneven distribution of point cloud, local planes are prone to tilt, resulting in registration errors. Therefore,
One indicator for evaluating autonomous vehicles’ capability is the accuracy of perceiving the surrounding environment. As an essential part of perception, MOT (multi-object tracking) algorithms provide vital guarantees for safe driving. However, many MOT algorithms based on the motion model only co
[1] Visual Simultaneous Localization and Mapping (SLAM) helps robots estimate their poses and perceive the environment in unknown settings. Recent work has demonstrated that implicit neural radiance fields and 3D Gaussian Splatting (3DGS) offer higher fidelity scene representation than traditional m
Accurate and robust LiDAR odometry is a crucial technology for robot localization. However, motion distortion and ranging error make it a bottleneck. Most existing methods are limited in accuracy and robustness because they simply compensate for motion distortion by constant velocity motion assumpti…
Simultaneous Localization and Mapping (SLAM) is widely used in applications such as robotics and autonomous driving, with methods involving multi-sensor fusion demonstrating excellent performance. However, they simply reject dynamic features and ignore the mutual benefits of self and dynamic objects…
Visual place recognition(VPR) is a crucial technology for autonomous driving and robotic navigation. However, severe appearance and perspective changes often lead to degradation of algorithm performance. Current methods mainly utilize single-modality RGB images, which are sensitive to environmental…
Recent advancements in neural implicit fields for Simultaneous Localization and Mapping (SLAM) have provided breakthroughs. However, the benefits of reconstruction results to the perception ability of robot are minimal. Therefore, we propose FI-SLAM, a dense semantic instance SLAM system based on ne…
The advancement of object detection algorithms has catalyzed the development of object-level semantic SLAM. However, due to missed and false detections, object-level semantic SLAM fails to represent the objects within the scene adequately. Therefore, this paper proposes a novel object-level semantic…
To achieve precise localization for autonomous vehicles and mitigate the problem of accumulated drift error in odometry, this paper proposes L-VIWO, a Visual-Inertial-Wheel Odometry based on lane lines. This method effectively utilizes the lateral constraints provided by lane lines to eliminate and…
Modern robotic systems are increasingly deployed in complex and diverse environments, and reliable localization under challenging conditions becomes crucial for the safe and efficient operation of these systems. The odometry based on LiDAR is prone to system collapse caused by computational divergen…
As a crucial part of mobile robotics and autonomous driving, Visual Place Recognition (VPR) is usually addressed by recognizing its similar reference images from a pre-obtained database. However, VPR always suffers from environmental changes, such as weather, illumination, perceptual-aliasing and so…
Visual place recognition (VPR) is a challenging issue for robotics and autonomous systems, focusing on utilizing visual information for robot localization. Currently, hierarchical architecture is being employed by growing works, which embraces RANSAC-based geometric verification for re-ranking. Howe…
NeRF can reconstruct incredibly realistic environmental maps in dense simultaneous localization and mapping, providing robots with more comprehensive scene map information. However, NeRF often struggles with geometric distortions in indoor reconstructions. To correct geometric distortions, we develo…
The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception.…
Efficient grasp pose detection is essential for robotic manipulation in cluttered scenes. However, most methods only utilize point clouds or images for prediction, ignoring the advantages of different features. In this paper, we present a multi-modal fusion network for joint segmentation and grasp p…
Real-time and robust long-term visual localization is a crucial technology for autonomous driving. Season and illumination variance make this problem more challenging. At present, most of excellent visual localization algorithms cannot run in real-time on devices with limited computing resources. In…
Semantic segmentation is a popular technology enabling robots to perceive and interact with the environment sufficiently. However, unseen objects in the new environment make it challenging to obtain accurate segmentation results. To solve this problem, we apply few-shot semantic segmentation in robo
The data fusion of camera, IMU, and wheel encoder measurements has proved its effectiveness in localizing ground robots, and obtaining accurate sensor extrinsic parameters is its premise. We propose an extrinsic parameter calibration algorithm and a multi-sensor-based pose estimation algorithm for t…
Object-oriented SLAM is a popular technology in autonomous driving and robotics. In this letter, we propose a stereo visual SLAM with a robust quadric landmark representation method.The system consists of four components, including deep learning detection, quadric landmark initialization, object dat
The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynam…
Semantic simultaneous localization and mapping (SLAM) is a popular technology enabling indoor mobile robots to sufficiently perceive and interact with the environment. In this paper, we propose an object-aware semantic SLAM system, which consists of a quadric initialization method, an object-level d
Relocalization is a critical component of robotics applications, it poses challenges due to changes in lighting conditions, weather, and viewing point. Image feature-based approaches are appearance-sensitive, high-level semantic landmark-based methods are ambiguous, and topological map matching-base
Semantic information and geometrical structures of a prior map can be leveraged in visual localization to bound drift errors and improve accuracy. In this paper, we propose SemLoc, a pure visual localization system, for accurate localization in a prior semantic map. To tightly couple semantic and st…
Loop closure detection has the potential to correct the drift of trajectories and build a global consistent map in LiDAR SLAM, however it remains a challenging problem in outdoor environment due to the sparsity of 3D point clouds data, large-scale scenes and moving objects. Inspired by the way human…
Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not always available, hence mining prior knowledge from the environment becomes a more…
Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By explo…