Semantic segmentation is a key technique that enables mobile robots to understand and navigate surrounding environments autonomously. However, most existing works focus on segmenting known objects, overlooking the identification of unknown classes, which is common in real-world applications. In this paper, we propose a feature-oriented framework for open-set semantic segmentation on LiDAR data, capable of identifying unknown objects while retaining the ability to classify known ones. We design a decomposed dual-decoder network to simultaneously perform closed-set semantic segmentation and generate distinctive features for unknown objects. The network is trained with multi-objective loss functions to capture the characteristics of known and unknown objects. Using the extracted features, we introduce an anomaly detection mechanism to identify unknown objects. By integrating the results of close-set semantic segmentation and anomaly detection, we achieve effective feature-driven LiDAR open-set semantic segmentation. Evaluations on both SemanticKITTI and nuScenes datasets demonstrate that our proposed framework significantly outperforms state-of-the-art methods. The source code will be made publicly available at https://github.com/nubot-nudt/DOSS.

Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achieving remarkable success. Recently, the emergence of end-to-end localization approaches has offered distinct advantages, including a streamlined system architecture and the elimination of the need to store extensive map data. Although these methods have demonstrated promising results, current end-to-end localization approaches still face limitations in robustness and accuracy. Bird’s-Eye-View (BEV) image is one of the most widely adopted data representations in autonomous driving. It significantly reduces data complexity while preserving spatial structure and scale consistency, making it an ideal representation for localization tasks. However, research on BEV-based end-to-end localization remains notably insufficient. To fill this gap, we propose BEVDiffLoc, a novel framework that formulates LiDAR localization as a conditional generation of poses. Leveraging the properties of BEV, we first introduce a specific data augmentation method to significantly enhance the diversity of input data. Then, the Maximum Feature Aggregation Module and Vision Transformer are employed to learn robust features while maintaining robustness against significant rotational view variations. Finally, we incorporate a diffusion model that iteratively refines the learned features to recover the absolute pose. Extensive experiments on the Oxford Radar RobotCar and NCLT datasets demonstrate that BEVDiffLoc outperforms the baseline methods. Our code is available at https://github.com/nubot-nudt/BEVDiffLoc.

PneuNet, consists of a series of interconnected chambers embedded within a soft elastomer material, can exhibit diverse deformations. 3D printing allows for precise control over both material combinations and geometrical configurations, enabling the fabrication of PneuNets with complicated structures and multifunctionality. However, the increased freedom in material and structures introduced by 3D printing also presents significant challenges for modeling and design, including material nonlinearities, complex cross-sections and varying initial curvatures. In this work, we develop 3D-printed PneuNets with varying initial curvatures and cross-sections demonstrating finite deformation with multiple complete turns. To model the helical shape, we establish a general nonlinear framework based on the minimum potential energy method. The model is validated by PneuNets with various material combinations and geometrical configurations across a range of constitutive models including Mooney-Rivlin, Ogden, Neo-Hookean and Yeoh models. Results show that the nonlinear model, especially the Mooney–Rivlin model, accurately captures the deformation without any fitting parameters, achieving an$R^{2}$value of 0.975, compared to 0.017 for the linear model. Based on the validated model, PneuNets are inverse-designed to achieve desired spatial deformations. Their dynamic responses and payload capacities are also evaluated. We design a 3D-printed octopus with tentacles composed of PneuNets, capable of mimicking the grasping and movement of a real octopus. Additionally, we demonstrate the multifunctional capabilities such as fluid transition and sensing. This study lays a solid foundation for the design and application of 3D-printed PneuNets.

High-resolution Earth observation, particularly from geostationary orbits (GEOs), requires the deployment of optical telescopes with apertures exceeding 10 m or more; however, a universally accepted solution to achieve this goal has yet to be formulated. This article proposes a high-resolution imaging method of checkerboard constellation based on optical pupil plane interferometry (PPI) and phase retrieval algorithms. An innovative solution is provided to address the issue of inadequate spatial frequency sampling in conventional sparse optical PPI: incorporating several checkerboard imagers and a monolithic telescope to create a checkerboard constellation that achieves an ultra-Nyquist sampling rate. Based on this sampling approach, the challenge of phase measurement can be resolved with phase recovery algorithms, which make it possible to generate high-resolution images comparable to that of a super-large-aperture traditional monolithic telescope based on modulus-only measurements. A checkerboard constellation is designed comprising four checkerboard imagers with a maximum baseline of 18 m and one conventional monolithic telescope with an aperture of 3.5 m, which achieves a twice Nyquist sampling rate and provides a ground resolution of 0.5 m at visible wavelengths in GEO. Simulations demonstrate that this setup can produce relatively optimal imaging quality when the signal-to-noise ratio (SNR) is higher than 40. An experiment conducted in the lab confirms the feasibility of this approach. The results show that: 1) high-resolution images can be produced by fusing the high-frequency data from the long-baseline checkerboard imagers with low-resolution data from the monolithic telescope and 2) using optical fibers as core components allows the equivalent aperture of telescopes to be extended to 10 m or even greater, demonstrating the potential scalability of this approach.

Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these instances have been labeled in the training set. This is important for safety-critical applications such as robust autonomous navigation. In this paper, we present a flexible and effective OIS framework for LiDAR point cloud that can accurately segment both known and unknown instances (i.e., seen and unseen instance categories during training). It first identifies points belonging to known classes and removes the back-ground by leveraging close-set panoptic segmentation networks. Then, we propose a novel ellipsoidal clustering method that is more adapted to the characteristic of LiDAR scans and allows precise segmentation of unknown instances. Furthermore, a diffuse searching method is proposed to handle the common over-segmentation problem presented in the known instances. With the combination of these techniques, we are able to achieve accurate segmentation for both known and unknown instances. We evaluated our method on the SemanticKITTI open-world LiDAR instance segmentation dataset. The experimental results suggest that it outperforms current state-of-the-art methods, especially with a 10.0% improvement in association quality. The source code of our method will be publicly available at https://github.com/nubot-nudt/ElC-OIS.

This article presents a sliding widow-based visual-inertial odometry to deal with the micro air vehicle (MAV) pose estimation problem. Errors caused by inertial measurement unit (IMU) preintegration, visual landmarks reprojection, and marginalization, are unified into a nonlinear residual minimization framework. Furthermore, a dual-step marginalization method has been proposed to increase the computational efficiency. Experiments have been conducted on publicly available datasets, as well as customized handheld and MAV platform, where state-of-the-art approaches have served as the baselines for comparison. According to the results, the proposed method has a comparative accuracy, which can run in real-time on an onboard minicomputer.