Neural Radiance Fields (NeRF) have shown significant potential in encoding complex 3D geometries and visual properties, making them a promising alternative to traditional dense SLAM systems. We propose Proud-SLAM, a dense SLAM framework that merges a neural point-based hybrid scene representation. Our key innovation is the HPoint method, which integrates color features into the point cloud, preserving real-world fidelity and enhancing scene rendering. Additionally, we design a hashV structure to efficiently manage and expand point clouds incrementally. Experiments on Replica and Scan-Net datasets demonstrate that Proud-SLAM excels in tracking precision, reconstruction accuracy, and scene rendering quality.

We introduce NeuV-SLAM, a novel dense simultaneous localization and mapping pipeline based on neural multiresolution voxels, characterized by ultra-fast convergence and incremental expansion capabilities. This pipeline utilizes RGBD images as input to construct multiresolution neural voxels, achieving rapid convergence while maintaining robust incremental scene reconstruction and camera tracking. Central to our methodology is to propose a novel implicit representation, termedVDFthat combines the implementation of neural signed distance field (SDF) voxels with an SDF activation strategy. This approach entails the direct optimization of color features and SDF values anchored within the voxels, substantially enhancing the rate of scene convergence. To ensure the acquisition of clear edge delineation, SDF activation is designed, which maintains exemplary scene representation fidelity even under constraints of voxel resolution. Furthermore, in pursuit of advancing rapid incremental expansion with low computational overhead, we developedhashMV, a novel hash-based multiresolution voxel management structure. This architecture is complemented by a strategically designed voxel generation technique that synergizes with a two-dimensional scene prior. Our empirical evaluations, conducted on the Replica and ScanNet Datasets, substantiate NeuV-SLAM's exceptional efficacy in terms of convergence speed, tracking accuracy, scene reconstruction, and rendering quality.

In recent years, Neural Radiation Fields (NeRF) have shown tremendous potential in encoding highly-detailed 3D geometry and environmental appearance, thus making it a promising alternative to traditional explicit maps for robot localization. However, current NeRF localization methods suffer from significant computational overheads, primarily resulting from the large number of iterations or particle samples required, as well as the additional computational demands associated with the estimation of the initial pose through multimodal sensors. To overcome these challenges, we propose a novel and time-efficient NeRF localization pipeline, named FVLoc-NeRF. This pipeline solely employs RGB monocular images as input and leverages a retrieval method to obtain the initial pose. Subsequently, the pose update is derived using the Perspective-n-Point (PnP) algorithm, thereby considerably reducing the number of iterations and accelerating the localization process. Our extensive experimental results clearly demonstrate that FVLoc-NeRF is much faster than the state-of-the-art method.

With the ever-increasing elderly population, elder walking assistance is in strong demand. Instead of receiving assistance from a human carer, a smart walker can bring an elder user a more convenient and autonomous walking experience. Towards intelligent and safe walking assistance, we propose a close-proximity front-following model for smart walkers, which analyzes the walking gait and detects the walking intention of the user, and intelligently follows the user in the front to provide walking support, without the user pushing the walker. We design a deep learning model named Front-Following Net (FFLNet), consisting of CNN and LSTM networks to extract spatial and temporal features of the elder walking gait, collected in time windows through a thermal camera and a 2D LiDAR, for effective walking intention detection. As compared to other walking intention detection approaches, our model can explore more effective information in the gait data within a short walking period, and achieve accurate hands-free tracking of the user. Experiments show that our FFLNet can achieve over 77% detection accuracy among six representative walking intentions and more than 90% accuracy for turning intentions. Combined with a carefully designed walker control policy, our smart walker can achieve high front-following correctness with the user.