Prediction Analysis of Optical Tracker Parameters using Machine Learning Approaches for efficient Head Tracking

A head tracker is a crucial part of the head mounted display systems, as it tracks the head of the pilot in the plane/cockpit simulator. The operational flaws of head trackers are also dependent on different environmental conditions like different lighting conditions and stray light interference. In this letter, an optical tracker has been employed to gather the 6-DoF data of head movements under different environmental conditions. Also, the effect of different environmental conditions and variation in distance between the receiver and optical transmitter on the 6-DoF data was analyzed.

Joint Forward-Backward Visual Odometry for Stereo Cameras

Visual odometry is a widely used technique in the field of robotics and automation to keep a track on the location of a robot using visual cues alone. In this paper, we propose a joint forward backward visual odometry framework by combining both, the forward motion and backward motion estimated from stereo cameras. The basic framework of LIBVIOS2 is used here for pose estimation as it can run in real-time on standard CPUs. The complementary nature of errors in the forward and backward mode of visual odometry helps in providing a refined motion estimation upon combining these individual estimates. In addition, two reliability measures, that is, forward-backward relative pose error and forward-backward absolute pose error have been proposed for evaluating visual odometry frameworks on its own without the requirement of any ground truth data. The proposed scheme is evaluated on the KITTI visual odometry dataset. The experimental results demonstrate improved accuracy of the proposed scheme over the traditional odometry pipeline without much increase in the system overload.

Evolution of Visual Odometry Techniques

With rapid advancements in the area of mobile robotics and industrial automation, a growing need has arisen towards accurate navigation and localization of moving objects. Camera based motion estimation is one such technique which is gaining huge popularity owing to its simplicity and use of limited resources in generating motion path. In this paper, an attempt is made to introduce this topic for beginners covering different aspects of vision based motion estimation task. The evolution of VO schemes over last few decades is discussed under two broad categories, that is, geometric and non-geometric approaches. The geometric approaches are further detailed under three different classes, that is, feature-based, appearance-based, and a hybrid of feature and appearance based schemes. The non-geometric approach is one of the recent paradigm shift from conventional pose estimation technique and is thus discussed in a separate section. Towards the end, a list of different datasets for visual odometry and allied research areas are provided for a ready reference.