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Robust odometry estimation for RGB-D cameras

• Christian Kerl, Jürgen Sturm, Daniel Cremers
• 2013 IEEE International Conference on Robotics…
• 2013

The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by… (More)

Dense visual SLAM for RGB-D cameras

• Christian Kerl, Jürgen Sturm, Daniel Cremers
• 2013 IEEE/RSJ International Conference on…
• 2013

In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this… (More)

Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions

• Erik Bylow, Jürgen Sturm, Christian Kerl, Fredrik Kahl, Daniel Cremers
• Robotics: Science and Systems
• 2013

The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method… (More)

Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences

• Frank Steinbrücker, Christian Kerl, Daniel Cremers
• 2013 IEEE International Conference on Computer…
• 2013

We propose a method to generate highly detailed, textured 3D models of large environments from RGB-D sequences. Our system runs in real-time on a standard desktop PC with a state-of-the-art graphics… (More)

CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM

• Lingni Ma, Christian Kerl, Jörg Stückler, Daniel Cremers
• 2016 IEEE International Conference on Robotics…
• 2016

Planes are predominant features of man-made environments which have been exploited in many mapping approaches. In this paper, we propose a real-time capable RGB-D SLAM system that consistently… (More)

Multi-view deep learning for consistent semantic mapping with RGB-D cameras

• Lingni Ma, Jörg Stückler, Christian Kerl, Daniel Cremers
• 2017 IEEE/RSJ International Conference on…
• 2017

Visual scene understanding is an important capability that enables robots to purposefully act in their environment. In this paper, we propose a novel deep neural network approach to predict semantic… (More)

Fast odometry and scene flow from RGB-D cameras based on geometric clustering

• Mariano Jaimez, Christian Kerl, Javier Gonzalez-Jimenez, Daniel Cremers
• 2017 IEEE International Conference on Robotics…
• 2017

In this paper we propose an efficient solution to jointly estimate the camera motion and a piecewise-rigid scene flow from an RGB-D sequence. The key idea is to perform a two-fold segmentation of the… (More)

Dense Continuous-Time Tracking and Mapping with Rolling Shutter RGB-D Cameras

• Christian Kerl, Jörg Stückler, Daniel Cremers
• 2015 IEEE International Conference on Computer…
• 2015

We propose a dense continuous-time tracking and mapping method for RGB-D cameras. We parametrize the camera trajectory using continuous B-splines and optimize the trajectory through dense, direct… (More)

Towards Illumination-Invariant 3D Reconstruction Using ToF RGB-D Cameras

• Christian Kerl, Mohamed Souiai, Jürgen Sturm, Daniel Cremers
• 2014 2nd International Conference on 3D Vision
• 2014

Creating textured 3D scans of indoor environments has experienced a large boost with the advent of cheap commodity depth sensors. However, the quality of the acquired 3D models is often impaired by… (More)

Odometry from RGB-D Cameras for Autonomous Quadrocopters

• Christian Kerl, FAKULTÄT FÜR INFORMATIK, Daniel Cremers, Markus Achtelik, Rachel Hornung
• 2012

This master’s thesis presents a robust, dense visual odometry method applicable to the stabilization of quadrocopters. In contrast to previous approaches using only sparse visual feature points in… (More)

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