This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinear factor graph optimization. While many existing approaches assume constant availability of one or multiple sensing signals, our methodology employs IMU-based pedestrian inertial navigation as the backbone for sensor fusion, opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), and WiFi signals when they are available in the environment. The proposed PEOPLEx framework is designed to incorporate sensing data as it becomes available, operating without any prior knowledge about the environment (e.g. anchor locations, radio frequency maps, etc.). Our contributions are twofold: 1) we introduce an opportunistic multi-sensor and real-time pedestrian positioning framework fusing the available sensor measurements; 2) we develop novel factors for adaptive scaling and coarse loop closures, significantly improving the precision of indoor positioning. Experimental validation confirms that our approach achieves accurate localization estimates in real indoor scenarios using commercial smartphones.

Loop closing and relocalization are crucial techniques to establish reliable and robust long-term SLAM by addressing pose estimation drift and degeneration. This article begins by formulating loop closing and relocalization within a unified framework. Then, we propose a novel multi-head network LCR-Net to tackle both tasks effectively. It exploits novel feature extraction and pose-aware attention mechanism to precisely estimate similarities and 6-DoF poses between pairs of LiDAR scans. In the end, we integrate our LCR-Net into a SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor driving environments. We thoroughly evaluate our LCR-Net through three setups derived from loop closing and relocalization, including candidate retrieval, closed-loop point cloud registration, and continuous relocalization using multiple datasets. The results demonstrate that LCR-Net excels in all three tasks, surpassing the state-of-the-art methods and exhibiting a remarkable generalization ability. Notably, our LCR-Net outperforms baseline methods without using a time-consuming robust pose estimator, rendering it suitable for online SLAM applications. To our best knowledge, the integration of LCR-Net yields the first LiDAR SLAM with the capability of deep loop closing and relocalization. The implementation of our methods will be made open-source.

Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction. Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM framework globally optimizing poses and 3D reconstruction in real-time. Robust pose estimation is at its core, supported by efficient loop closing and online full bundle adjustment, which optimize per frame by utilizing the learned global geometry of the complete history of input frames. Simultaneously, we update the implicit and continuous surface representation on-the-fly to ensure global consistency of 3D reconstruction. Results on various synthetic and real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art approaches at tracking robustness and reconstruction accuracy. Furthermore, GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.

According to WHO statistics, the number of visually impaired people is increasing annually. One of the most critical necessities for visually impaired people is the ability to navigate safely. This paper proposes a navigation system based on the visual slam and Yolo algorithm using monocular cameras. The proposed system consists of three steps: obstacle distance estimation, path deviation detection, and next-step prediction. Using the ORB-SLAM algorithm, the proposed method creates a map from a predefined route and guides the users to stay on the route while notifying them if they deviate from it. Additionally, the system utilizes the YOLO algorithm to detect obstacles along the route and alert the user. The experimental results, obtained by using a laptop camera, show that the proposed system can run in 30 frame per second while guiding the user within predefined routes of 11 meters in indoors and outdoors. The accuracy of the positioning system is 8cm, and the system notifies the users if they deviate from the predefined route by more than 60 cm.

This paper presents a novel visual feature based scene mapping method for underwater vehicle manipulator systems (UVMSs), with specific emphasis on robust mapping in natural seafloor environments. Our method uses GPU accelerated SIFT features in a graph optimization framework to build a feature map. The map scale is constrained by features from a vehicle mounted stereo camera, and we exploit the dynamic positioning capability of the manipulator system by fusing features from a wrist mounted fisheye camera into the map to extend it beyond the limited viewpoint of the vehicle mounted cameras. Our hybrid SLAM method is evaluated on challenging image sequences collected with a UVMS in natural deep seafloor environments of the Costa Rican continental shelf margin, and we also evaluate the stereo only mode on a shallow reef survey dataset. Results on these datasets demonstrate the high accuracy of our system and suitability for operating in diverse and natural seafloor environments. We also contribute these datasets for public use.