11 accepted papers
Visual localization in complex environments remains a critical challenge for robotics and AR applications. Sequential localization, where pose estimates are refined over time, is important for autonomous agents. However, traditional methods often require storing extensive image databases or point cl…
Robots need to know where they are in the world to operate effectively without human support. One common first step for precise robot localization is visual place recognition. It is a challenging problem, especially when the output is required in an online fashion, and the current state-of-the-art a
Mobile robots on construction sites require accurate pose estimation to perform autonomous surveying and inspection missions. Localization in construction sites is a particularly challenging problem due to the presence of repetitive features such as flat plastered walls and perceptual aliasing due t
"We introduce the task of localizing an input image within a multi-modal reference map represented by a collection of 3D scene graphs. These scene graphs comprise multiple modalities, including object-level point clouds, images, attributes, and relationships between objects, offering a lightweight a…
Our world is non-static, and robots should be able to track its changing geometry. For tracking changes, data asso-ciations between 3D points over time are key. In this paper, we investigate the problem of associating 3D points on plant organs from different mapping runs over time while the plants g…
State estimation is a key ingredient in most robotic systems. Often, state estimation is performed using some form of least squares minimization. Basically, all error minimization procedures that work on real-world data use robust kernels as the standard way for dealing with outliers in the data. Th
Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues gene…
Visual place recognition is a challenging task, especially in outdoor environments as the scenes naturally change their appearance. In this letter, we propose a method for visual place recognition that is able to deal with seasonal changes, different weather condition as well as illumination changes
Maps are an important component of most robotic navigation systems and building maps under uncertainty is often referred to as simultaneous localization and mapping or SLAM. Most SLAM approaches start from scratch and build a map only based on their own observations and odometry information. In this…
Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under dif
The ability to localize a robot is an important capability and matching of observations under substantial changes is a prerequisite for robust long-term operation. This paper investigates the problem of efficiently coping with seasonal changes in image data. We present an extension of a recent appro…