Qualitative real-time range extraction for preplanned scene partitioning using laser beam coding

Abstract

This paper proposes a novel technique to extract range using a phase-only filter for a laser beam. The workspace is partitioned according to M meaningful preplanned range segments, each representing a relevant range segment in the scene. The phase-only filter codes the laser beam into M different diffraction patterns, corresponding to the predetermined range of each segment. Once the scene is illuminated by the coded beam, each plane in it would irradiate in a pattern corresponding to its range from the light source. Thus, range can be extracted at acquisition time. This technique has proven to be very efficient for qualitative real-time range extraction, and is mostly appropriate to handle mobile robot applications where a scene could be partitioned into a set of meaningful ranges, such as obstacle detection and docking. The hardware consists of a laser beam, a lens, a filter, and a camera, implying a simple and cost-effective technique.

Introduction

Range estimation is a basic requisite in Computer Vision, and thus, has been explored to a great extent. One can undoubtedly find a large quantity of range estimation techniques. These techniques vary in characteristics, such as: density, accuracy, cost, speed, size, and weight. Each technique could be suitable to a group of application, and at the same time, completely inappropriate to others. Therefore, the decision of matching the best technique usually depends on the specific requirements of the desired application. For example: 3D modeling of an object might need both dense and accurate estimation, where cost and speed would not be critical. On the contrary, dense and accurate estimation might have less importance in collision-free path planning, where cost, speed, and mobility, would be essential.

Range sensing techniques can be divided into two categories: passive and active (Jarvis, 1983, Jarvis, 1993). Passive sensing refers to techniques using the environmental light conditions, such that do not impose artificial energy sources. These techniques include: range from focus/defocus, range from attenuating medium, range from texture, range from stereo, and range from motion. Active sensing refers to techniques that impose structured energy sources, such as: light, ultrasound, X-ray, and microwave. These techniques include: ultrasonic range sensors, radar range sensors, laser sensors (time-of-flight), range from brightness, pattern light range sensors (triangulation), grid coding, and Moiré fringe range contours.

The technique presented here fits in the pattern light category. Pattern light is commonly used in a stereo configuration in order to facilitate the correspondence procedure, which forms the challenging part of triangulation. Usually, one camera is replaced by a device that projects pattern light (also known as ‘structure light’), while the scene is grabbed by the other camera. A very popular group of techniques are known as ‘coded structured light’. The coding is achieved either by projecting a single pattern or a set of patterns. The main idea is that the patterns are designed in such a way that each pixel is assigned with a codeword (Salvi et al., 2004). There is a direct mapping between the codeword of a specific pixel and its corresponding coordinates, so correspondence becomes trivial. Different types of patterns are used for the coding process, such as: black and white, gray scale, and RGB (Caspi et al., 1998; Horn and Kiryati, 1999; Manabe et al., 2002; Pages et al., 2003; Sato and Inokuchi, 1987; Valkenburg and McIvor, 1998). Coded structure light is considered one of the most reliable techniques for estimating range, but since usually a set of patterns is needed, it is not applicable to dynamic scenes. When using only one pattern, dynamic scenes might be allowed, but the results are usually of poor resolution.

Additional techniques implementing structured light to assist the correspondence procedure include sinusoidal varying intensities, stripes of different types (e.g. colored, cut), and projected grids (Albamont and Goshtasby, 2003; Fofi et al., 2003; Furukawa and Kawasaki, 2003; Guisser et al., 2000; Je et al., 2004; Kang et al., 1995; Maruyama and Abe, 1993; Scharstein and Szeliski, 2003). These methods, although projecting only one pattern, still exploit a time consuming search procedure.

Recently, efforts to estimate range using pattern light and only one image were made. In (Winkelbach and Wahl, 2002), objects were illuminated with a stripes pattern, and surface orientation was first estimated from the directions and the width of the stripes. Then shape was reconstructed from orientations. The drawback of this technique is that it works only for a single object, and the reconstruction is relative, i.e. no absolute range is known. In (Lee et al., 1999), Objects were illuminated with a sinusoidal pattern, and depth was calculated from the frequency variation. The drawback of this technique is its heavy computational time.

Here, pattern light is used with only one image to directly estimate range. No correspondence (triangulation) is needed, and the setup consists only of a laser beam, a lens, a single mask, and a camera. The main concept would be to partition the workspace into a set of range segments, in a way that would be meaningful for a working mobile robot. The motivation lies in the fact that in order to perform tasks such as obstacle detection or docking, it should be sufficient that the robot would be able to distinguish between a set of predefined ranges. The idea is to code a laser beam into different patterns, where each pattern corresponds to a specific range segment. Once a scene is illuminated by the coded beam, each patch in it would irradiate with the pattern that corresponds to its range from the light source. The beam coding is merely realized by one special phase-only filter, and consequently, the technique is accurate, fast (hardware solution), cost-effective, and in addition, fits to dynamic scenes.