Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collection, informative path planning must be computed online and be responsive to new observations. In this work, we present IA-TIGRIS (Incremental and Adaptive Tree-based Information Gathering Using Informed Sampling), which is an incremental and adaptive sampling-based informative path planner designed for real-time onboard execution. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated belief maps. We additionally present detailed implementation and optimization insights to facilitate real-world deployment, along with an array of reward functions tailored to specific missions and behaviors. Extensive simulation results demonstrate IA-TIGRIS generates higher-quality paths compared to baseline methods. We validate our planner on two distinct hardware platforms: a hexarotor unmanned aerial vehicle (UAV) and a fixed-wing UAV, each having different motion models and configuration spaces. Our results show up to a 38% improvement in information gain compared to baseline methods, highlighting the planner's potential for deployment in real-world applications. Project website: ia-tigris.github.io.

Depth cues are essential for visual perception tasks like Salient Object Detection (SOD). Due to varying depth reliability across scenes, some researchers propose evaluating the overall quality of the depth maps and discarding the less reliable ones to avoid contamination. However, these methods often fail to fully utilize valuable information in depth maps, leading to sub-optimal performance particularly when depth quality is unreliable. Since low-quality depth maps still contain useful information that potentially improves model performance, we propose a Depth Pixel-wise Potential-aware Network to leverage these depth cues effectively. This network includes two novel components designed: 1) A learning strategy for explicitly modeling the confidence of each depth pixel to assist the model in locating valid information in the depth map. 2) A cross-modal adaptive multiple fusion module that fuses features from both RGB and depth modalities. It aims to mitigate the contamination effect of unreliable depth maps and fully exploit the benefits of multiple fusion strategies. Experimental results show that on four publicly available datasets, our method outperforms 17 mainstream methods on various evaluation metrics.

Capturing sufficient global context and rich spatial structure information is critical for dense prediction tasks. Convolutional Neural Network (CNN) is particularly adept at modeling fine-grained local features, while Transformer excels at modeling global context information. It is evident that CNN and Transformer exhibit complementary characteristics. Exploring the design of a network, that efficiently fuses these two models to leverage their strengths fully and achieve more accurate detection, represents a promising and worthwhile research topic. In this paper, we introduce a novel CNN-Transformer Iterative Fusion Network (CTIF-Net) for salient object detection. It efficiently combines CNN and Transformer to achieve superior performance by using a parallel dual encoder structure and a feature iterative fusion module. Firstly, CTIF-Net extracts features from the image using the CNN and the Transformer, respectively. Secondly, two feature convertors and a feature iterative fusion module are employed to combine and iteratively refine the two sets of features. The experimental results on multiple SOD datasets show that CTIF-Net outperforms 17 state-of-the-art methods, achieving higher performance in various mainstream evaluation metrics such as F-measure, S-measure, and MAE value. The code will be publicly available.