15 accepted papers
Determining the precise geographic location of an image at a global scale remains an unsolved challenge. Standard image retrieval techniques are inefficient due to the sheer volume of images (>100M) and fail when coverage is insufficient. Scalable solutions, however, involve a trade-off: global cla…
Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving. However, these maps have limitations: they lack detail, often contain inaccuracies, and are difficult to create and maintain, especially in an automated fashion. Can we use _raw image…
Large-scale visual localization systems continue to relyon 3D point clouds built from image collections usingstructure-from-motion. While the 3D points in these modelsare represented using local image features, directly match-ing a query image's local features against the point cloud ischallenging d…
Outlier rejection and equivalently inlier set optimization is a key ingredient in numerous applications in computer vision such as filtering point-matches in camera pose estimation or plane and normal estimation in point clouds. Several approaches exist, yet at large scale we face a combinatorial ex…
The growing popularity of autonomous systems creates a need for reliable and efficient metric pose retrieval algorithms. Currently used approaches tend to rely on nearest neighbor search of binary descriptors to perform the 2D-3D matching and guarantee realtime capabilities on mobile platforms. Thes…
Robust and accurate visual-inertial estimation is crucial to many of today's challenges in robotics. Being able to localize against a prior map and obtain accurate and drift-free pose estimates can push the applicability of such systems even further. Most of the currently available solutions, howeve
Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational powe…
Place recognition is a core component in simultaneous localization and mapping (SLAM), limiting positional drift over space and time to unlock precise robot navigation. Determining which previously visited places belong together continues to be a highly active area of research as robotic application
An accurate estimate of the 3D-structure in the environment is key to robotic applications such as autonomous inspection, obstacle avoidance and manipulation. Recent years have seen substantial algorithmic advances towards creating highly accurate models of small objects as well as large scale archi
Having a team of robots to perform a task such as mapping is faster and more reliable than doing the same with a single robot, which can be crucial in scenarios such as search and rescue. We are developing a fully distributed framework for collaborative mapping with large robot swarms that is robust…
Accurately estimating a robot's pose relative to a global scene model and precisely tracking the pose in real-time is a fundamental problem for navigation and obstacle avoidance tasks. Due to the computational complexity of localization against a large map and the memory consumed by the model, state…
Robust, scalable localization unlocks path-planning, obstacle avoidance as well as manipulation and thus is a core competency for many robotic applications. However, as we leave the lab and move out in the world, models of the environment no longer span distances of meters but kilometers in length.…
Large scale, long-term, distributed mapping is a core challenge to modern field robotics. Using the sensory output of multiple robots and fusing it in an efficient way enables the creation of globally accurate and consistent metric maps. To combine data from multiple agents into a global map, most e…
Localization is essential for robots to operate autonomously, especially for extended periods of time, when estimator drift tends to destroy alignment to any global map. Though there has been extensive work in vision-based localization in recent years, including several systems that show real-time p…
Robust, scalable place recognition is a core competency for many robotic applications. However, when revisiting places over and over, many state-of-the-art approaches exhibit reduced performance in terms of computation and memory complexity and in terms of accuracy. For successful deployment of robo…