The discovery of ocean worlds such as Enceladus, Titan, and Europa motivates the development of versatile autonomous mobility systems to enable the next era of space exploration where there is large uncertainty in terrain specifications due to a lack of prior surface reconnaissance missions. To explore these environments, we propose Exobiology Extant Life Surveyor (EELS): the first large-scale (4 lm long with 400 Nm peak torque) snake robot. The large scale is achieved by using a screw-based active skin mechanism to decouple motion and shape control. Autonomous mobility for such a system remains an open problem due to its many Degrees of Freedom (DoFs), complex terrain interactions, and intermittent localization failures in GPS-denied perceptually degraded environments due to the presence of fog, dust, featureless terrains, etc. We propose NEO, an autonomy architecture that scales to large DoFs to generate a versatile set of gaits to achieve mobility in unknown extreme environments. We also discuss the resilience capabilities of NEO that achieves closed-loop tracking performance by leveraging exteroception when available but can also operate with proprioception only, leading to resiliency against localization failures via graceful degradation in performance rather than unsafe behaviors. A quantitative hardware evaluation of exteroceptive leader-follower gait is performed indoors on synthetic ice along with qualitative results of field deployment of the proprioceptive leader-follower and sidewinding gaits in extreme environments of icy and sandy terrains with mobility-stressing elements such as trenches, undulations, and steep slopes (up to 35 degrees). We present a set of lessons learned from field deployments with a summary of challenges and open research problems. Video: www.rohanthakker.in/eels-neo-autonomy.html

Virtually navigating through photos from a 3D image-based reconstruction has recently become very popular in many applications. In this paper, we consider a particular virtual travel maneuver that is important for this type of virtual navigation---orbiting to photos that can see a point-of-interest (POI). The main challenge with this particular type of orbiting is how to give appropriate feedback to the user regarding the existence and information of each photo in 3D while allowing the user to manipulate three degrees-of-freedom (DoF) for orbiting around the POI. We present a hybrid approach that combines features from two baselines---proxy plane and thumbnail approaches. Experimental results indicate that users rated our hybrid approach more favorably for several qualitative questionnaire statements, and that the hybrid approach is preferred over both baselines for outdoor scenes.

Drawing annotations with 3D hand gestures in augmented reality is useful for creating visual and spatial references in the real world, especially when these gestures can be issued from a distance. Different techniques exist for highlighting physical objects with hand-drawn annotations from a distance, assuming an approximate 3D scene model (e.g., as provided by the Microsoft HoloLens). However, little is known about user preference and performance of such methods for annotating real-world 3D environments. To explore and evaluate different 3D hand-gesture-based annotation drawing methods, we have developed an annotation drawing application using the HoloLens augmented reality development platform. The application can be used for highlighting objects at a distance and multi-user collaboration by annotating in the real world.

Navigating through a virtual, 3D reconstructed scene has recently become very important in many applications. A popular approach is to virtually travel to the photos used in reconstructing the scene; such an approach may be generally termed a "snapping-to-photos" virtual travel interface. While previous work has either used fully constrained interfaces (always at the photos) or minimally constrained interfaces (free-flight navigation), in this paper we introduce new snapping-to-photos interfaces that lie in between these two extremes. Our snapping-to-photos interfaces snap the view to a photo in 3D based on viewpoint similarity and optionally the user's mouse cursor or finger-tap position. Experimental results, with both indoor and outdoor scene reconstructions, found that our snapping-to-photos interfaces are preferred over the baseline fully constrained-to-photos interface, that there exist differences between indoor and outdoor scenes, and that users preferred and were able to reach target photos better with click-to-snap point-of-interest snapping compared to automatic point-of-view snapping.

Augmented Reality (AR) applications may require the precise alignment of virtual objects to the real world. We propose automatic alignment of virtual objects to physical constraints calculated from the real world in real time ("snapping to reality"). We demonstrate SnapToReality alignment techniques that allow users to position, rotate, and scale virtual content to dynamic, real world scenes. Our proof-of-concept prototype extracts 3D edge and planar surface constraints. We furthermore discuss the unique design challenges of snapping in AR, including the user's limited field of view, noise in constraint extraction, issues with changing the view in AR, visualizing constraints, and more. We also report the results of a user study evaluating SnapToReality, confirming that aligning objects to the real world is significantly faster when assisted by snapping to dynamically extracted constraints. Perhaps more importantly, we also found that snapping in AR enables a fresh and expressive form of AR content creation.

We present a method for collaborative augmented reality (AR) that enables users from different viewpoints to interpret object references specified via 2D on-screen circling gestures. Based on a user's 2D drawing annotation, the method segments out the userselected object using an incomplete or imperfect scene model and the color image from the drawing viewpoint. Specifically, we propose a novel segmentation algorithm that utilizes both 2D and 3D scene cues, structured into a three-layer graph of pixels, 3D points, and volumes (supervoxels), solved via standard graph cut algorithms. This segmentation enables an appropriate rendering of the user's 2D annotation from other viewpoints in 3D augmented reality. Results demonstrate the superiority of the proposed method over existing methods.

Augmented reality enhanced collaboration systems often allow users to draw 2D gesture annotations onto video feeds to help collaborators to complete physical tasks. This works well for static cameras, but for movable cameras, perspective effects cause problems when trying to render 2D annotations from a new viewpoint in 3D. In this paper, we present a new approach towards solving this problem by using gesture enhanced annotations. By first classifying which type of gesture the user drew, we show that it is possible to render annotations in 3D in a way that conforms more to the original intention of the user than with traditional methods. We first determined a generic vocabulary of important 2D gestures for remote collaboration by running an Amazon Mechanical Turk study with 88 participants. Next, we designed a novel system to automatically handle the top two 2D gesture annotations - arrows and circles. Arrows are handled by identifying their anchor points and using surface normals for better perspective rendering. For circles, we designed a novel energy function to help infer the object of interest using both 2D image cues and 3D geometric cues. Results indicate that our approach outperforms previous methods in terms of better conveying the original drawing's meaning from different viewpoints.

We present a novel 2D gesture annotation method for use in image-based 3D reconstructed scenes with applications in collaborative virtual and augmented reality. Image-based reconstructions allow users to virtually explore a remote environment using image-based rendering techniques. To collaborate with other users, either synchronously or asynchronously, simple 2D gesture annotations can be used to convey spatial information to another user. Unfortunately, prior methods are either unable to disambiguate such 2D annotations in 3D from novel viewpoints or require relatively dense reconstructions of the environment.

In Augmented Reality (AR) based remote collaboration, a remote user can draw a 2D annotation that emphasizes an object of interest to guide a local user accomplishing a task. This annotation is typically performed only once and then sticks to the selected object in the local user's view, independent of his or her camera movement. In this paper, we present an algorithm to segment the selected object, including its occluded surfaces, such that the 2D selection can be appropriately interpreted in 3D and rendered as a useful AR annotation even when the local user moves and significantly changes the viewpoint.

We propose a novel formulation for determining the absolute pose of a single or multi-camera system given a known vertical direction. The vertical direction may be easily obtained by detecting the vertical vanishing points with computer vision techniques, or with the aid of IMU sensor measurements from a smartphone. Our solver is general and able to compute absolute camera pose from two 2D-3D correspondences for single or multi-camera systems. We run several synthetic experiments that demonstrate our algorithm's improved robustness to image and IMU noise compared to the current state of the art. Additionally, we run an image localization experiment that demonstrates the accuracy of our algorithm in real-world scenarios. Finally, we show that our algorithm provides increased performance for real-time model-based tracking compared to solvers that do not utilize the vertical direction and show our algorithm in use with an augmented reality application running on a Google Tango tablet.

We present a system that supports an augmented shared visual space for live mobile remote collaboration on physical tasks. The remote user can explore the scene independently of the local user's current camera position and can communicate via spatial annotations that are immediately visible to the local user in augmented reality. Our system operates on off-the-shelf hardware and uses real-time visual tracking and modeling, thus not requiring any preparation or instrumentation of the environment. It creates a synergy between video conferencing and remote scene exploration under a unique coherent interface. To evaluate the collaboration with our system, we conducted an extensive outdoor user study with 60 participants comparing our system with two baseline interfaces. Our results indicate an overwhelming user preference (80%) for our system, a high level of usability, as well as performance benefits compared with one of the two baselines.

Augmented reality annotations and virtual scene navigation add new dimensions to remote collaboration. In this paper, we present a touchscreen interface for creating freehand drawings as world-stabilized annotations and for virtually navigating a scene reconstructed live in 3D, all in the context of live remote collaboration. Two main focuses of this work are (1) automatically inferring depth for 2D drawings in 3D space, for which we evaluate four possible alternatives, and (2) gesture-based virtual navigation designed specifically to incorporate constraints arising from partially modeled remote scenes. We evaluate these elements via qualitative user studies, which in addition provide insights regarding the design of individual visual feedback elements and the need to visualize the direction of drawings.