Learning spatial models from sensor data raises the challenging data association problem of relating model parameters to individual measurements. This paper proposes an EM-based algorithm, which solves the model learning ...

In this paper, we study the problem of going from a real-world, multi-agent system to the generation of control programs in an automatic fashion. In particular, a computer vision system is presented, capable of ...

We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause ...

6DOF Pose tracking is useful in many contexts, e.g., in augmented reality (AR) applications. In particular, we seek to assist visually impaired persons by providing them with an auditory interface to their environment through ...

Approximate inference by sampling from an appropriately constructed posterior has recently seen a dramatic increase in popularity in both the robotics and computer vision community. In this paper, I will describe a number ...

While robot mapping has seen massive strides recently, higher level abstractions in map representation are still not widespread. Maps containing semantic concepts such as objects and labels are essential for many tasks ...

Environmental change is a growing international concern, calling for the regular monitoring, studying and preserving of detailed information about the evolution of underwater ecosystems. For example, fragile coral reefs ...

We present a novel variant of the RANSAC algorithm that is much more efficient, in particular when dealing with problems with low inlier ratios. Our algorithm assumes that there exists some grouping in the data, based ...

Large-scale 3D reconstruction has recently received much attention from the computer vision community. Bundle adjustment is a key component of 3D reconstruction problems. However, traditional bundle adjustment ...

We propose a spectral partitioning approach for large-scale optimization problems, specifically structure from motion. In structure from motion, partitioning methods reduce the problem into smaller and better conditioned ...