Indoor Topological Localization Based on a Novel Deep Learning Technique

Millions of people in the world suffer from vision impairment or vision loss. Traditionally, they rely on guide sticks or dogs to move around and avoid potential obstacles. However, both guide sticks and dogs are passive. They are unable to provide conceptual knowledge or semantic contents of an environment. To address this issue, this paper presents a vision-based cognitive system to support the independence of visually impaired people. More specifically, a 3D indoor semantic map is firstly constructed with a hand-held RGB-D sensor. The constructed map is then deployed for indoor topological localization. Convolutional neural networks are used for both semantic information extraction and location inference. Semantic information is used to further verify localization results and eliminate errors. The topological localization performance can be effectively improved despite significant appearance changes within an environment. Experiments have been conducted to demonstrate that the proposed method can increase both localization accuracy and recall rates. The proposed system can be potentially deployed by visually impaired people to move around safely and have independent life

Visual-LiDAR SLAM Based on Unsupervised Multi-channel Deep Neural Networks

Recently, deep learning techniques have been applied to solve visual or light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) problems. Supervised deep learning SLAM methods need ground truth data for training, but collecting such data is costly and labour-intensive. Unsupervised training strategies have been adopted by some visual or LiDAR SLAM methods. However, these methods only exploit the potential of single-sensor modalities, which do not take the complementary advantages of LiDAR and visual data. In this paper, we propose a novel unsupervised multi-channel visual-LiDAR SLAM method (MVL-SLAM) which can fuse visual and LiDAR data together. Our SLAM system consists of an unsupervised multi-channel visual-LiDAR odometry (MVLO) component, a deep learning–based loop closure detection component, and a 3D mapping component. The visual-LiDAR odometry component adopts a multi-channel recurrent convolutional neural network (RCNN). Its input consists of front, left, and right view depth images generated from 360 ∘ 3D LiDAR data and RGB images. We use the features from a deep convolutional neural network (CNN) for the loop closure detection component. Our SLAM method does not require ground truth data for training and can directly construct environmental 3D maps from the 3D mapping component. Experiments conducted on the KITTI odometry dataset have shown the rotation and translation errors are lower than some of the other unsupervised methods, including UnMono, SfmLearner, DeepSLAM, and UnDeepVO. Experimental results show that our methods have good performance. By fusing visual and LiDAR data, MVL-SLAM has higher accuracy and robustness of the pose estimation compared with other single-modal SLAM systems