The Simultaneous Localization and Map** (SLAM) technique has achieved astonishing
progress over the last few decades and has generated considerable interest in the …

Combining multiple LiDARs enables a robot to maximize its perceptual awareness of
environments and obtain sufficient measurements, which is promising for simultaneous …

The most common way to deal with the uncertainty present in noisy sensorial perception and
action is to model the problem with a probabilistic framework. Maximum likelihood …

We present a novel way of odometry estimation from Velodyne LiDAR point cloud scans.
The aim of our work is to overcome the most painful issues of Velodyne data-the sparsity …

We consider the problem of autonomous mobile robot exploration in an unknown
environment, taking into account a robot's coverage rate, map uncertainty and state …

In this paper, we introduce a novel and efficient technique for consistent covariance recovery
in nonlinear optimization-based Visual-Inertial Navigation Systems (VINS). Estimating …

In this letter, we propose a low-cost, high-efficiency framework for cooperative active pose-
graph simultaneous localization and map** (SLAM) for multiple robots in three …

We develop a computationally efficient approach for evaluating the information-theoretic
term within belief space planning (BSP), where during belief propagation the state vector …

Efficient algorithms exist to obtain a sparse 3D representation of the environment. Bundle
adjustment (BA) and structure from motion (SFM) are techniques used to estimate both the …

This chapter reviews the concept of pose-graph simultaneous localization and map**
(SLAM) for underwater navigation. We show that pose-graph SLAM is a generalized …