12 accepted papers
Enabling robots to robustly follow leaders supports tasks such as carrying supplies or guiding customers. While existing methods often fail to generalize to arbitrary leaders, and struggle when the leader temporarily leaves the robot’s field of view, this work presents a unified framework to address…
Underwater acoustic communication, characterized by limited bandwidth, high latency, and low reliability, poses significant challenges for data exchange in bathymetric collaborative simultaneous localization and mapping (CSLAM). In this article, we introduce a novel vector quantization (VQ) method c…
In crowd navigation, the local goal plays a crucial role in trajectory initialization, optimization, and evaluation. Recognizing that when the global goal is distant, the robot's primary objective is avoiding collisions, making it less critical to pass through the exact local goal point, this work i
High-fidelity reconstruction is crucial for dense SLAM. Recent popular methods utilize 3D Gaussian splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods ignore issues of detail and consistency in different parts of the scene. To address this, we p
In the context of indoor crowd navigation for mobile robots, the generation of spatial-temporally complete trajectories gives the robot a wider range of options, thereby improving the robustness and safety of navigation. Focused on this topic, this letter presents an incremental optimization framewo
6D object pose estimation holds essential roles in various fields, particularly in the grasping of industrial workpieces. Given challenges like rust, high reflectivity, and absent textures, this paper introduces a point cloud based pose estimation framework (PS6D). PS6D centers on slender and multi-…
Loop closure, as one of the crucial components in SLAM, plays an essential role in correcting accumulated errors. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data, making them less versatile and robust in real-w
Existing bathymetric simultaneous localization and mapping (SLAM) methods predominantly rely on odometry information for loop closure detection, which has a deteriorating performance when handling unreliable odometry data or conducting large-scale mapping missions. This letter introduces a novel gen
In human-aware navigation, the robot tacitly games with humans, balancing safety and efficiency according to human intentions. Poor balance or bad intent recognition causes the robot to stop conservatively or advance rashly, resulting in a deadlock or even a collision respectively. To address the is…
Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the le…
In the narrow lane scene of autonomous driving, it is critical for the ego car to recognize the intentions of social vehicles and cooperate with them. However, cooperating with social vehicles is challenging due to insufficient information. This paper proposes an Explorative Game that adopts Partici…
Mammogram mass detection is of great clinical significance due to its high proportion in breast cancers. The information from cross views (i.e., mediolateral oblique and cranio-caudal) is highly related and complementary, and is helpful to make comprehensive decisions. However, unlike radiologists w…