Vision-based localization systems, namely visual odometry (VO) and visual inertial odometry
(VIO), have attracted great attention recently. They are regarded as critical modules for …

Monocular visual odometry provides more robust functions on navigation and obstacle
avoidance for mobile robots than other visual odometries, such as binocular visual …

Abstract Simultaneous Localization and Mapping (SLAM) is a key problem in the field of
Artificial Intelligence and mobile robotics that addresses the problem of localization and …

We propose a real-time method for odometry and mapping using range measurements from
a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are …

Combining visual and inertial measurements has become popular in mobile robotics, since
the two sensing modalities offer complementary characteristics that make them the ideal …

In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We
recorded a large set of image sequences from a Microsoft Kinect with highly accurate and …

The development of a navigation system is one of the major challenges in building a fully
autonomous platform. Full autonomy requires a dependable navigation capability not only in …

In this paper, we focus on the problem of motion tracking in unknown environments using
visual and inertial sensors. We term this estimation task visual–inertial odometry (VIO), in …

We present a data processing pipeline to online estimate ego‐motion and build a map of the
traversed environment, leveraging data from a 3D laser scanner, a camera, and an inertial …

Visual odometry (VO) is the process of estimating the egomotion of an agent (eg, vehicle,
human, and robot) using only the input of a single or multiple cameras attached to it …