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We propose a direct (featureless) monocular SLAM algorithm which, in contrast to current state-of-the-art regarding direct methods , allows to build large-scale, consistent maps of the environment. Along with highly accurate pose estimation based on direct image alignment , the 3D environment is reconstructed in real-time as pose-graph of keyframes with(More)
We propose a fundamentally novel approach to real-time visual odometry for a monocular camera. It allows to benefit from the simplicity and accuracy of dense tracking - which does not depend on visual features - while running in real-time on a CPU. The key idea is to continuously estimate a semi-dense inverse depth map for the current frame, which in turn(More)
In this paper, we describe a system that enables a low-cost quadrocopter coupled with a ground-based laptop to navigate autonomously in previously unknown and GPS-denied environments. Our system consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion and state estimation and a PID controller to generate steering(More)
Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry - represented as inverse depth in a reference frame -(More)
We propose a novel Large-Scale Direct SLAM algorithm for stereo cameras (Stereo LSD-SLAM) that runs in real-time at high frame rate on standard CPUs. In contrast to sparse interest-point based methods, our approach aligns images directly based on the photoconsistency of all high-contrast pixels, including corners, edges and high texture areas. It(More)
We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over(More)
Figure 1: From left to right: AR demo application with simulated car. Corresponding estimated semi-dense depth map. Estimated dense collision mesh, fixed and shown from a different perspective. Photo of running system. The attached video shows the system in action. ABSTRACT We present a direct monocular visual odometry system which runs in real-time on a(More)
— We present a 25 g nano-quadrotor equipped with a micro PAL-camera and wireless video transmitter, with which we demonstrate autonomous hovering and figure flying using a visual-inertial SLAM system running on a ground-based laptop. To our knowledge this is the lightest quadrotor capable of visual-inertial navigation with off-board processing. Further we(More)
— We present an approach that enables a low-cost quadrocopter to accurately fly various figures using vision as main sensor modality. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion and state estimation and a PID controller to generate steering commands. Our system is able to navigate in(More)
We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods. It contains 50 real-world sequences comprising more than 100 minutes of video, recorded across dozens of different environments – ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting(More)