Understanding the perceived scene during navigation enables intelligent robot behaviors. Current vision-based semantic SLAM (Simultaneous Localization and Mapping) systems provide these capabilities. However, their performance decreases in visually-degraded environments, that are common places for critical robotic applications, such as search and rescue missions. In this paper, we present SIGNAV, a real-time semantic SLAM system to operate in perceptually-challenging situations. To improve the robustness for navigation in dark environments, SIGNAV leverages a multi-sensor navigation architecture to fuse vision with additional sensing modalities, including an inertial measurement unit (IMU), LiDAR, and wheel odometry. A new 2.5D semantic segmentation method is also developed to combine both images and LiDAR depth maps to generate semantic labels of 3D mapped points in real time. We demonstrate that the navigation accuracy from SIGNAV in a variety of indoor environments under both normal lighting and dark conditions. SIGNAV also provides semantic scene understanding capabilities in visually-degraded environments. We also show the benefits of semantic information to SIGNAV’s performance.

We propose a new approach that utilizes semantic information to register 2D monocular video frames to the world using 3D georeferenced data, for augmented reality driving applications. The geo-registration process uses our predicted vehicle pose to generate a rendered depth map for each frame, allowing 3D graphics to be convincingly blended with the real world view. We also estimate absolute depth values for dynamic objects, up to 120 meters, based on the rendered depth map and update the rendered depth map to reflect scene changes over time. This process also creates opportunistic global heading measurements, which are fused with other sensors, to improve estimates of the 6 degrees-of- freedom global pose of the vehicle over state-of-the-art outdoor augmented reality systems [5]-, [19]. We evaluate the navigation accuracy and depth map quality of our system on a driving vehicle within various large-scale environments for producing realistic augmentations.

This paper introduces a user-worn Augmented Reality (AR) based first-person weapon shooting system (AR-Weapon), suitable for both training and gaming. Different from existing AR-based first-person shooting systems, AR-Weapon does not use fiducial markers placed in the scene for tracking. Instead it uses natural scene features observed by the tracking camera from the live view of the world. The AR-Weapon system estimates 6-degrees of freedom orientation and location of the weapon and of the user operating it, thus allowing the weapon to fire simulated projectiles for both direct fire and non-line of sight during live runs. In addition, stereo cameras are used to compute depth and provide dynamic occlusion reasoning. Using the 6-DOF head and weapon tracking, dynamic occlusion reasoning and a terrain model of the environment, the fully virtual projectiles and synthetic avatars are displayed on the user's head mounted Optical-See-Through (OST) display overlaid over the live view of the real world. Since the projectiles, weapon characteristics and virtual enemy combatants are all simulated they can easily be changed to vary scenarios, new projectile types and future weapons. In this paper, we present the technical algorithms, system design and experiment results for a prototype AR-Weapon system.