Robust 2D Mapping Integrating with 3D Information for the Autonomous Mobile Robot Under Dynamic Environment

Abstract

In order to move around automatically, mobile robots usually need to recognize their working environment first. Simultaneous localization and mapping (SLAM) has become an important research field recently, by which the robot can generate a map while moving around. Both two-dimensional (2D) mapping and three-dimensional (3D) mapping methods have been developed greatly with high accuracy. However, 2D maps cannot reflect the space information of the environment and 3D mapping needs long processing time. Moreover, conventional SLAM methods based on grid maps take a long time to delete the moving objects from the map and are hard to delete the potential moving objects. In this paper, a 2D mapping method integrating with 3D information based on immobile area occupied grid maps is proposed. Objects in 3D space are recognized and their space information (e.g., shapes) and properties (moving objects or potential moving objects like people standing still) are projected to the 2D plane for updating the 2D map. By using the immobile area occupied grid map method, recognized still objects are reflected to the map quickly by updating the immobile area occupancy probability with a high coefficient. Meanwhile, recognized moving objects and potential moving objects are not used for updating the map. The unknown objects are reflected to the 2D map with a lower immobile area occupancy probability so that they can be deleted quickly once they are recognized as moving objects or start to move. The effectiveness of our method is proven by experiments of mapping under dynamic indoor environment using a mobile robot.

Research on Design and Motion Control of a Considerate Guide Mobile Robot for Visually Impaired People

“…The guide mobile robot needs to generate a path that avoids the potential occupied spaces of objects on the way and move to the destination naturally. In addition, there are cases where the robot tries to enter under a desk or chair to get to the destination in a shorter way [26]. The robot may mistakenly think that it can pass through the places under the obstacles since there is enough space for the robot.…”

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

“…There are many ways to describe the SLAM problem represented in the literature [17][18][19][20][21][22][23][24]. Currently, the most common way is to define SLAM as a probability density function, which can be described by the following generic form [25][26][27]:…”

“…Such a form of expression (1), where wanted values are reconstructed for all previous states, is called "Full SLAM". The opposite approach, where only a recent position is estimated, called "Online SLAM", can be calculated by recursive integration [23]. It is possible to apply Bayes and Markov rules to (1) and define the probability density function as a recursive process of predictions and corrections of the robot's localization in the map m, which depends on motion (kinematic constrains and controls) and observation models [19]:…”

“…It divides the workspace into a series of grids, where each cell in the grid corresponds to binary random variables that describe the occupancy probability [8,20,[40][41][42]. Hence, the map m is given by a product occupancy probability of each cell mi, which is associated with a certain point in three-dimensional Cartesian space [23,34]:…”