A new terrain mapping method for mobile robots obstacle negotiation

Abstract

This paper introduces a new terrain mapping method for mobile robots with a 2-D laser rangefinder. In the proposed method, an elevation map and a certainty map are built and used for the filtering of erroneous data. The filter, called Certainty Assisted Spatial (CAS) filter, first employs the physical constraints on motion continuity and spatial continuity to distinguish corrupted pixels (e.g., due to artifacts, random noise, or the "mixed pixels" effect) and missing data from uncorrupted pixels in an elevation map. It then removes the corrupted pixels and missing data, while missing data is filled in by a Weighted Median filter. Uncorrupted pixels are left intact so as to retain edges of objects. Our extensive indoor and outdoor mapping experiments demonstrate that the CAS filter has better performance in erroneous data reduction and map detail preservation than existing filters.

Semantic Mapping Using Mobile Robots

“…This is an important problem when one is exploring an unknown terrain. Applications for terrain mapping range from path planning and local obstacle avoidance to detection of changes in the terrain and object recognition [32]. Planetary exploration is an interesting example of practical application for this research topic [21].…”

A method for mobile robot navigation on rough terrain

“…There are two closely related issues in ON-terrain mapping and path planning [1]. This paper relates to path planning and it assumes that a terrain map is available from our recently developed terrain mapping method [2,3].…”

“…The terrain mapping module (see [2] for the details) produces a terrain map for obstacle negotiation. In our system, this map is represented by a 2-dimensional (2-D) array or "grid," in which each element or "cell" holds a value representing the height of the terrain at that cell.…”

Behavior maps for online planning of obstacle negotiation and climbing on rough terrain

“…To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide which parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON) [21]. While this problem has been mainly addressed for semi-rough terrain, e.g.…”