In this paper, we introduce AE-FABMAP, a new self-supervised bag of words-based SLAM method. We also present AE-ORB-SLAM, a modified version of the current state of the art BoW-based path planning algorithm. That is, we have used a deep convolutional autoencoder to find loop closures. In the context of bag of words visual SLAM, vector quantization (VQ) is considered as the most time-consuming part of the SLAM procedure, which is usually performed in the offline phase of the SLAM algorithm using unsupervised algorithms such as Kmeans++. We have addressed the loop closure detection part of the BoW-based SLAM methods in a self-supervised manner, by integrating an autoencoder for doing vector quantization. This approach can increase the accuracy of large-scale SLAM, where plenty of unlabeled data is available. The main advantage of using a self-supervised is that it can help reducing the amount of labeling. Furthermore, experiments show that autoencoders are far more efficient than semi-supervised methods like graph convolutional neural networks, in terms of speed and memory consumption. We integrated this method into the state of the art long range appearance based visual bag of word SLAM, FABMAP2, also in ORB-SLAM. Experiments demonstrate the superiority of this approach in indoor and outdoor datasets over regular FABMAP2 in all cases, and it achieves higher accuracy in loop closure detection and trajectory generation.

The kidnapping of Tennessee teen Elizabeth Thomas allegedly by his ex-teacher Tad Cummins will be the point of discussion at Friday's 20/20 show. The show will focus on Cummins' months on the run and what will happen next to the 50-year-old, who was arrested mid-April. The 15-year-old former student of Cummins went missing in March and was found in a remote part of northern California, more than a month after her disappearance. Elizabeth was reunited with her family, while Cummins was arrested. He has been charged with kidnapping and transporting a minor across state lines with the intention to engage in sexual activity.

One night in 1982, John Mumford was working on an avalanche patrol on an icy Colorado mountain pass when the van carrying him and two other men slid off the road and plunged over a cliff. The other guys were able to walk away, but Mumford had broken his neck. The lower half of his body was paralyzed, and though he could bend his arms at the elbows, he could no longer grasp things in his hands. Fifteen years later, however, he received a technological wonder that reactivated his left hand. It was known as the Freehand System. A surgeon placed a sensor on Mumford's right shoulder, implanted a pacemaker-size device known as a stimulator just below the skin on his upper chest, and threaded wires into the muscles of his left arm.