Livelihood assets have a significant impact on the livelihood options and income of tourism communities. The level of tourism development and the type of natural tourism communities often exhibit spatial differences. By using the method of sustainable livelihood and examining the main factors that affect community income from the perspective of spatial heterogeneity, it is possible to identify the livelihood assets that play the most critical role in the development of sustainable livelihoods and income generation in the community, which helps provide more reasonable advice on tourism destination management. In this article, we conducted a case study on 16 communities in and around Jiuzhaigou Nature Reserve in Sichuan Province, China, and divided the communities into three types, i.e., core attraction areas, service support areas, and secondary service support areas, according to the geographical and spatial characteristics, tourism development level, and livelihood type. The participatory assessment method was adopted to obtain in-depth interviews and questionnaire data from 256 rural households, which was utilized to construct four multivariate regression models to investigate the effect of livelihood assets on community income and the spatial difference of the factors that affect community income. The results showed that (1) livelihood assets exert a significant impact on community income in tourist areas, with a remarkable spatial difference; (2) the types of livelihoods and the effective utilization of livelihood assets have a significant impact on farmers’ incomes; (3) the type and amount of livelihood assets have a certain impact on the choice of livelihood; and (4) a farmer’s livelihood type determines the utilization method and the effective utilization of livelihood assets.

The role that authenticity plays in tourism is debatable. Researchers have focused on the nature of authenticity, while the natural landscape is taken for granted as authentic. “Landscape” is the symbolic environment created by human acts of conferring meaning to nature and the environment. The concept of nature authenticity is proposed to enrich the connotation of authenticity. A structural equation model was used to analyze the relationships between motivation, perceived authenticity, destination image and satisfaction. The results indicate that perceived authenticity positively affects tourists’ perceptions of the destination image in both the context of nature (β = 0.51, p < 0.01) and the cultural context (β = 0.65, p < 0.01). Perceived authenticity also has positive effects on the satisfaction for both the natural (β = 0.42, p < 0.01) and cultural (β = 0.20, p < 0.01) aspects. Tourists valued both nature and culture authenticity in the Chinese context. As a newly emerged phenomenon, performing arts act as an effective way to demonstrate local culture and history. The moderating effect of a performing art show was discussed with model comparison. Tourists who watched the performing arts show thought highly of the authenticity of the destination, especially regarding the cultural aspect.

The traditional simultaneous localization and mapping (SLAM) systems rely on the assumption of a static environment and fail to accurately estimate the system’s location when dynamic objects are present in the background. While learning-based dynamic SLAM systems have difficulties in handling unknown moving objects, geometry-based methods have limited success in addressing the residual effects of unidentified dynamic objects on location estimation. To address these issues, we propose an anti-dynamics two-stage RGB-D SLAM approach. In the first stage, we identify potential motion regions for both known and unknown dynamic objects and rapidly generate pose estimates through optical flow tracking and model generation techniques. In the second stage, dynamic features within each frame are eliminated through dynamic assessment. For unidentified dynamic objects, we propose an approach involving superpixel extraction and geometric clustering to delineate potential motion regions based on color and geometric cues within the image. We conducted extensive experiments using public datasets and real-world scenarios, which demonstrated that our method surpasses current state-of-the-art (SOTA) dynamic SLAM techniques on public datasets. Our method’s robustness was also confirmed through experiments in real scenes featuring objects moving at varying speeds.

This letter introduces a novel framework for dense Visual Simultaneous Localization and Mapping (VSLAM) based on Gaussian Splatting. Recently, SLAM based on Gaussian Splatting has shown promising results. However, in monocular scenarios, the Gaussian maps reconstructed lack geometric accuracy and exhibit weaker tracking capability. To address these limitations, we jointly optimize sparse visual odometry tracking and 3D Gaussian Splatting scene representation for the first time. We obtain depth maps on visual odometry keyframe windows using a fast Multi-View Stereo (MVS) network for the geometric supervision of Gaussian maps. Furthermore, we propose a depth smooth loss and Sparse-Dense Adjustment Ring (SDAR) to reduce the negative effect of estimated depth maps and preserve the consistency in scale between the visual odometry and Gaussian maps. We have evaluated our system across various synthetic and real-world datasets. The accuracy of our pose estimation surpasses existing methods and achieves state-of-the-art. Additionally, it outperforms previous monocular methods in terms of novel view synthesis and geometric reconstruction fidelities.

It is important for underwater wireless sensor networks (UWSNs) to satisfy the diverse monitoring demands in harsh and perilous three-dimensional underwater environments. After the monitoring missions and demands transform, a large number of underwater event coverage holes will appear. Traditional network repair strategies cannot be applied to the ever-changing underwater monitoring missions and the harsh multi-constrained three-dimensional underwater environments. Multiple autonomous underwater vehicles (multi-AUVs) have strong adaptability and flexibility in perilous and harsh three-dimensional underwater environments. First, an underwater event coverage hole (UECH) repair model under various constraints is proposed. Next, a multi-agent event coverage hole repair algorithm (MECHR), which combines multi-agent strategy with diversity archive strategy, is proposed to repair UECHs in UWSNs. The presented algorithm symmetrically completes subtasks through information exchange and interactive operations with other agents. Unlike existing repair strategies, the MECHR algorithm can effectively repair a large number of UECHs resulted by the transformations in underwater monitoring scenes and demands. The MECHR algorithm can adapt to a wide range of harsh scenes and multi-constrained three-dimensional underwater environments. Eventually, the effect of the MECHR algorithm is verified through underwater repair simulation experiments, which can adapt to the constantly changing three-dimensional underwater monitoring environments.