Coverage path planning (CPP) of multiple Dubins robots has been extensively applied in aerial monitoring, marine exploration, and search and rescue. Existing multi-robot coverage path planning (MCPP) research use exact or heuristic algorithms to address coverage applications. However, several exact algorithms always provide precise area division rather than coverage paths, and heuristic methods face the challenge of balancing accuracy and complexity. This paper focuses on the Dubins MCPP problem of known environments. Firstly, we present an exact Dubins multi-robot coverage path planning (EDM) algorithm based on mixed linear integer programming (MILP). The EDM algorithm searches the entire solution space to obtain the shortest Dubins coverage path. Secondly, a heuristic approximate credit-based Dubins multi-robot coverage path planning (CDM) algorithm is presented, which utilizes the credit model to balance tasks among robots and a tree partition strategy to reduce complexity. Comparison experiments with other exact and approximate algorithms demonstrate that EDM provides the least coverage time in small scenes, and CDM produces a shorter coverage time and less computation time in large scenes. Feasibility experiments demonstrate the applicability of EDM and CDM to a high-fidelity fixed-wing unmanned aerial vehicle (UAV) model.

The emerging event cameras are bio-inspired sensors that can output pixel-level brightness changes at extremely high rates, and event-based visual-inertial odometry (VIO) is widely studied and used in autonomous robots. In this paper, we propose an event-based stereo VIO system, namely ESVIO. Firstly, we present a novel direct event-based VIO method, which fuses events’ depth, Time-Surface images, and pre-integrated inertial measurement to estimate the camera motion and inertial measurement unit (IMU) biases in a sliding window non-linear optimization framework, effectively improving the state estimation accuracy and robustness. Secondly, we design an event-inertia semi-joint initialization method, through two steps of event-only initialization and event-inertia initial optimization, to rapidly and accurately solve the initialization parameters of the VIO system, thereby further improving the state estimation accuracy. Based on these two methods, we implement the ESVIO system and evaluate the effectiveness and robustness of ESVIO on various public datasets. The experimental results show that ESVIO achieves good performance in both accuracy and robustness when compared with other state-of-the-art event-based VIO and stereo visual odometry (VO) systems, and, at the same time, with no compromise to real-time performance.

Prompt tuning provides a low-cost way of adapting vision-language models (VLMs) for various downstream vision tasks without requiring updating the huge pre-trained parameters. Dispensing with the conventional manual crafting of prompts, the recent prompt tuning method of Context Optimization (CoOp) introduces adaptable vectors as text prompts. Nevertheless, several previous works point out that the CoOp-based approaches are easy to overfit to the base classes and hard to generalize to novel classes. In this paper, we reckon that the prompt tuning works well only in the base classes because of the limited capacity of the adaptable vectors. The scale of the pre-trained model is hundreds times the scale of the adaptable vector, thus the learned vector has a very limited ability to absorb the knowledge of novel classes. To minimize this excessive overfitting of textual knowledge on the base class, we view prompt tuning as learning to learn (LoL) and learn the prompt in the way of meta-learning, the training manner of dividing the base classes into many different subclasses could fully exert the limited capacity of prompt tuning and thus transfer it power to recognize the novel classes. To be specific, we initially perform fine-tuning on the base class based on the CoOp method for pre-trained CLIP. Subsequently, predicated on the fine-tuned CLIP model, we carry out further fine-tuning in an N-way K-shot manner from the perspective of meta-learning on the base classes. We finally apply the learned textual vector and VLM for unseen classes.Extensive experiments on benchmark datasets validate the efficacy of our meta-learning-informed prompt tuning, affirming its role as a robust optimization strategy for VLMs.

In this letter, we introduce PolarMesh as a star-convex approximation of a 3D object based on spherical projection and can be applied to monocular object pose and shape estimation. The proposed PolarMesh can be stored in a discrete 2D map that allows a trivial conversion between it and the object surface, as the adjacent map elements form faces of the 3D model. The fixed-grid 2D nature of this object surface approximation ensures that it can be estimated from an image patch of the object with an auto-encoder neural network. Moreover, the PolarMesh not only encodes the object shape but also its orientation. Consequently, it is used for accurate estimation of object orientation and can be extended to the full 6D pose estimation using a projection-based method for translation. An exhaustive quantitative evaluation on LineMod, LineMod-Occlusion and HomebrewedDB shows the competitive performance of the proposed method compared to other monocular RGB pose estimation pipelines. Furthermore, we tested the generalization capabilities of the representation on the recent NOCS dataset for category-level pose estimation and achieved very compelling results in terms of predicted rotations.

Precise annotation of 6-D poses in real data is intricate and time-consuming, however, an essential requirement to train pose estimation pipelines. We propose a way for scalable, end-to-end 6-D pose regression with weak supervision to avoid this problem. Our method requires neither 3-D models nor 6-D object poses as ground truth. Instead, we use 2-D bounding boxes and object sizes as the only labels and constrain the problem with multiple images of known relative poses during training. A novel Rotated-IoU loss brings together a pose prediction from an image with labeled 2-D bounding boxes of the corresponding object in other views. Our rotation estimation combines an initial coarse pose classification with an offset regression using a continuous rotation parametrization that allows for direct pose estimation. At test time, the model still uses only a single image to predict a 6-D pose. We observe that multi-view constraints and our rotation representation used during training lead to better learning of 6-D pose embeddings in comparison to fully supervised methods. Experiments on several datasets show that the proposed method is capable of predicting poses of good quality, in spite being trained with only weak labels. Direct pose regression without the need for a consecutive refinement stage thereby ensures real-time performance.