SLAM with SC - PHD Filters
نویسندگان
چکیده
By Chee Sing Lee, Sharad Nagappa, Narcis Palomeras, Daniel E. Clark, and Joaquim Salvi An Underwater Vehicle Application T he random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position.
منابع مشابه
Tradeoffs in SLAM with Sparse Information Filters
Designing filters exploiting the sparse information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the m...
متن کاملThe Study of Improving Kalman Filters Family for Nonlinear SLAM
When Extended Kalman Filter is used to solve the SLAM problem of a nonlinear system, the linearization error will lead to severe estimation error or even make the method to be divergent. After analyzing the linearization principle of Kalman filters family, two improved methods are suggested to decrease the linearization error. These two methods improve posterior estimation accuracy by revising ...
متن کاملExtended target tracking using PHD filters
The world in which we live is becoming more and more automated, exemplified by the numerous robots, or autonomous vehicles, that operate in air, on land, or in water. These robots perform a wide array of different tasks, ranging from the dangerous, such as underground mining, to the boring, such as vacuum cleaning. In common for all different robots is that they must possess a certain degree of...
متن کاملSLAM Using EKF , EH ∞ and Mixed EH 2 / H ∞ Filter
The process of simultaneously building the map and locating a vehicle is known as Simultaneous Localization and Mapping (SLAM) and can be used for autonomous navigation. The estimation of vehicle states and landmarks plays an important role in SLAM. Most of the SLAM algorithms are based on extended Kalman filters (EKFs). However, Kalman filters are not the best choice for SLAM as they suffer fr...
متن کاملPlanar Features and 6D-SLAM based on Linear Regression Kalman Filters with n-Dimensional Approximated Gaussians
In this paper, a six-dimensional (6D) Simultaneous Localization and Mapping (SLAM) based on novel Linear Regression Kalman Filter (LRKF), called Smart Sampling Kalman Filter (S2KF), is proposed. While the conventional feature based SLAM methods use point features as landmarks, only a few take the advantage of geometric information like corners, edges, and planes. A feature based SLAM method usi...
متن کامل