Image moments for higher-level feature based navigation

Abstract

This paper presents a novel vision-based localization and mapping algorithm using image moments of region features. The environment is represented using regions, such as planes and/or 3D objects instead of only a dense set of feature points. The regions can be uniquely defined using a small number of parameters; e.g., a plane can be completely characterized by normal vector and distance to a local coordinate frame attached to the plane. The variation of image moments of the regions in successive images can be related to the parameters of the regions. Instead of tracking a large number of feature points, variations of image moments of regions can be computed by tracking the segmented regions or a few feature points on the objects in successive images. A map represented by regions can be characterized using a minimal set of parameters. The problem is formulated as a nonlinear filtering problem. A new discrete-time nonlinear filter based on the state-dependent coefficient (SDC) form of nonlinear functions is presented. It is shown via Monte-Carlo simulations that the new nonlinear filter is more accurate and consistent than EKF by evaluating the root-mean squared error (RMSE) and normalized estimation error squared (NEES).

Monocular-based pose determination of uncooperative space objects

“…Finally in [21], an EKF-SLAM-based method for real-time relative state estimation of uncooperative unknown spinning targets using stereo vision is proposed. Although the use of the Kalman filter and its variations is popular among the SLAM and relative navigation field, there are some prior works looking into deriving a nonlinear observer for improved filter stability and robustness with nonlinear dynamics and measurement models [24,25].…”

“…Features can be scalars or vector quantities. It is possible to extract region features [25], line or curve features [12], and point features [13]. In the context of pose determination, correspondence or feature matching entails the problem of matching the features extracted in one image of the target with corresponding features of another image, or identifiable points in a model when available.…”

Vision‐based Localization and Robot‐centric Mapping in Riverine Environments

“…In Fallon, Papadopoulos, Leonard, & Patrikalakis (), a surface‐craft equipped with an acoustic modem is used to support localization of autonomous underwater vehicles. A departure from point‐feature based SLAM is reported using higher‐level features represented by Bzier curves as stereo vision primitives (Rao, Chung, & Hutchinson, ) or tracking the image moments of region features (Dani, Panahandeh, Chung, & Hutchinson, ) with a new stochastic nonlinear estimator (Dani, Chung, & Hutchinson, ).…”

Visual‐inertial curve simultaneous localization and mapping: Creating a sparse structured world without feature points

“…Various SLAM algorithms have incorporated high‐level features in order to overcome drawbacks associated with point‐based SLAM. Examples of high‐level features include planes, image moments, line segments, objects such as office chairs and tables, or a river . A desirable characteristic of high‐level structure is that it provides a compact structured map of the environment.…”