20 accepted papers
Robotic systems demand accurate and comprehensive 3D environment perception, requiring simultaneous capture of photo-realistic appearance (optical), precise layout shape (geometric), and open-vocabulary scene understanding (semantic). Existing methods typically achieve only partial fulfillment of th…
In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this cha…
Planar structures, ubiquitous in man-made indoor environments, enable compact and accurate scene abstraction for various downstream tasks. Recent methods distill planar features into learning-based MVS geometries to obtain coherent 3D plane estimation from multi-view inputs. However, the lack of exp…
Scalable robot learning is hindered by the high cost of acquiring diverse, high-quality embodied data. Existing data generation approaches partially mitigate this issue but typically depend on hard-to-access hardware and labor-intensive manual effort, with limited generalization to diverse scene con
Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves training a neural network to regress the signed distance from any point to the nearest obstacle, which has the advantages of lightweight storage and continuous querying. However, existing algorithms usually rely on conflictin
In daily domestic settings, frequently used objects like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cups</i> often have unfixed positions and multiple instances within the same category, and their carriers also frequently change. As a result, it
Open-vocabulary scene understanding is critical for robotics, yet existing 3D Gaussian Splatting (3DGS) methods rely on compressed feature embeddings, compromising semantic fidelity and fine-grained interpretation. Although utilizing uncompressed high-dimensional features offers a potential solution
Multi-agent distributed collaborative mapping provides comprehensive and efficient representations for robots. However, existing approaches lack instance-level awareness and semantic understanding of environments, limiting their effectiveness for downstream applications. To address this issue, we pr
In recent years, there has been a surge of interest in open-vocabulary 3D scene reconstruction facilitated by visual language models (VLMs), which showcase remarkable capabilities in open-set retrieval tasks. Although the semantic ambiguity of existing point-wise feature maps is alleviated by open-v
In everyday life, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, th
In recent years, vision-language models (VLMs) have advanced open-vocabulary mapping, enabling mobile robots to simultaneously achieve environmental reconstruction and high-level semantic understanding. While integrated object cognition helps mitigate semantic ambiguity in point-wise feature maps, e
Loop closure detection and pose estimation play a significant role in correcting odometry trajectories and generating globally consistent point cloud maps. Geometric feature descriptor methods neglect object-level spatial topology features, resulting in inadequate performance in loop closure detecti
Recently the dense Simultaneous Localization and Mapping (SLAM) based on neural implicit representation has shown impressive progress in hole filling and high-fidelity mapping. Nevertheless, existing methods either heavily rely on known scene bounds or suffer inconsistent reconstruction due to drift…
Collaborative mapping aids agents in achieving an efficient and comprehensive understanding of their environment. Recently, there has been growing interest in using neural networks as maps to represent functions that implicitly define the geometric features of a scene. However, existing implicit map
Environment representations endowed with sophisticated semantics are pivotal for facilitating seamless interaction between robots and humans, enabling them to effectively carry out various tasks. Open-vocabulary representation, powered by Visual-Language models (VLMs), possesses inherent advantages,
Filtering out outlier data associations between local maps can improve the robustness and accuracy of multi-robot localization. When the overlap is low and the field of view difference is large, it is likely to produce outlier data associations between local maps, which will reduce the matching accu…
Capturing the semantics of objects and the topological relationship allows the robot to describe the scene more intelligently like a human and measure the similarity between scenes (loop closure detection) more accurately. However, many current semantic graph matching methods are based on walk descr…
The collaborative operation of multiple robots can make up for the shortcomings of a single robot, such as limited field of perception or sensor failure. multirobots collaborative semantic mapping can enhance their comprehensive contextual understanding of the environment. However, existing multirob…
Autonomous robots are often required to acquire high-level prior knowledge by continuously reconstructing the semantics and geometry of the surrounding scene, which is the basis of exploration and planning. Most existing continuous semantic mapping algorithms cannot distinguish potential differences…