Occupancy-elevation grid: an alternative approach for robotic mapping and navigation

Abstract

SUMMARYThis paper proposes an alternative environment mapping method for accurate robotic navigation based on 3D information. Typical techniques for 3D mapping using occupancy grid require intensive computational workloads in order to both build and store the map. This work introduces an Occupancy-Elevation Grid (OEG) mapping technique, which is a discrete mapping approach where each cell represents the occupancy probability, the height of the terrain and its variance. This representation allows a mobile robot to know with an accurate degree of certainty whether a place in the environment is occupied by an obstacle and the height of such obstacle. Thus, based on its hardware characteristics, it can make calculations to decide if it is possible to traverse that specific place. In general, the map representation introduced can be used in conjunction with any kind of distance sensor. In this work, we use laser range data and stereo system data with a probabilistic treatment. The resulting maps allow the execution of tasks as decision making for autonomous navigation, exploration, localization and path planning, considering the existence and the height of the obstacles. Experiments carried out with real data demonstrate that the proposed approach yields useful maps for autonomous navigation.

High-Level Path Planning for an Autonomous Sailboat Robot Using Q-Learning

“…Stelzer [20] defines long-term path planning for autonomous sailboat as the use of a global weather map (wind, currents and/or other environmental forces) to find optimal routes to the target while short-term path planning uses only local and instantaneous information about the weather. Local path planning is usually reactive, to allow avoidance of non-static and non-mapped obstacles, while long-term path planning is deliberative in the sense that it uses a global map and occupancy-grid-based algorithms [21] to find a particular path to the objective. Determining the global path (also called weather routing) for a sailboat is of extreme importance in long range missions since guidance based only on local and instantaneous wind information can lead to undesired and potentially catastrophic situations.…”

A Versatile Method for Depth Data Error Estimation in RGB-D Sensors

“…Besides a model for the non-systematic error cannot be determined, a limit for it can be eventually estimated and this can be useful in applications as probabilistic robotics, in order to perform visual localization and mapping [ 7 , 8 ]. An application that needs a prior knowledge of the error estimation is the one dealing with mapping and navigation with a stereo camera [ 7 , 8 ]. In this application, the authors need to apply a sensor model in which the variance of the measured elevation should be used.…”

“…Also, two applications (in simple depth maps correction and point clouds registration) are devised and implemented just to illustrate the utility of the depth RMS errors resulting from our approach. Other applications as wearable sensors [ 6 ] or probabilistic robotics [ 7 , 8 ] can also benefit from the RMS error estimation provided by our approach. In this last specific application, previously determined error bounding in function of the distance to points in the environment are used in order to estimate the variance in the depth provided by stereo cameras, which is used for mapping and reconstruction.…”

Air-Ground Cooperative Environment Modeling with Bounded Elevation Map

“…Recently, many studies have focused on processing environmental data from single robot such as unmanned aerial vehicles (UAVs),unmanned ground vehicles (UGVs), unmanned surface vehicles(USVs) and even remote operated vehicles (ROVs) to achieve better perception [1]. Because of the less processing requirements and storages space of the elevation map [2], a lot of researchers utilize elevation map method to build map for path planning in real time. For example, Kleiner [3] et al have described a Kalman filter based approach for building elevation maps.…”