Light-stripe Rangefinder Research Articles

ハンドスイープされたスリット輝線の幾何拘束に基づく三次元計測

We propose a new method for measuring the entire surfaces of a 3D object by light stripe range finding. Most of light striping measurements assume that the positions of a camera and light sources are calibrated previously. The propose method can utilize not known positions of light sources but unconstraint free wands. In the case of plural parallel planes, there is a geometric constraint between the plane generated by the freehand light stripe and the intersected points with the parallel light stripes, so that the 3D geometry of the freehand light source can be determined analytically. Once hand-sweeping the slit light source over the object, we can obtain the entire 3D shapes of the object. It can expand the variety of applications on shape measurements.

Perception for collision avoidance and autonomous driving

The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The earlier work of the group concentrated on road following, cross-country driving, and obstacle detection. The new focus is on short-range sensing, to look all around the vehicle for safe driving. The current system uses video sensing, laser rangefinders, a novel light-stripe rangefinder, software to process each sensor individually, a map-based fusion system, and a probability based predictive model. The complete system has been demonstrated on the Navlab 11 vehicle for monitoring the environment of a vehicle driving through a cluttered urban environment, detecting and tracking fixed objects, moving objects, pedestrians, curbs, and roads.

Occlusions as a guide for planning the next view

A strategy for acquiring 3-D data of an unknown scene, using range images obtained by a light stripe range finder is addressed. The foci of attention are occluded regions, i.e., only the scene at the borders of the occlusions is modeled to compute the next move. Since the system has knowledge of the sensor geometry, it can resolve the appearance of occlusions by analyzing them. The problem of 3-D data acquisition is divided into two subproblems due to two types of occlusions. An occlusion arises either when the reflected laser light does not reach the camera or when the directed laser light does not reach the scene surface. After taking the range image of a scene, the regions of no data due to the first kind of occlusion are extracted. The missing data are acquired by rotating the sensor system in the scanning plane, which is defined by the first scan. After a complete image of the surface illuminated from the first scanning plane has been built, the regions of missing data due to the second kind of occlusions are located. Then, the directions of the next scanning planes for further 3-D data acquisition are computed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Integrated sensor and range-finding analog signal processor

The authors present experimental results from an array of cells, each of which contains a photodiode and the analog signal-processing circuitry needed for light-stripe range finding. Prototype circuits were fabricated through MOSIS in a 2- mu m CMOS p-well double-metal, double-poly process. This design builds on some of the ideas that have been developed for ICs that integrate signal-processing circuitry with photosensors. In the case of light-stripe range finding, the increase in cell complexity from sensing only to sensing and processing makes the modification of the operational principle of range finding practical, which in turn results in a dramatic improvement in performance. The IC array of photosensor and analog signal processor cells that acquires 1000 frames of light-stripe range data per second-two orders of magnitude faster than conventional light-stripe range-finding methods. The highly parallel range-finding algorithm used requires that the output of each photosensor site be continuously monitored. Prototype high-speed range-finding systems have been built using a 5*5 array and a 28*32 array of these sensing elements.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Autonomous scene description with range imagery

This paper presents a program to produce object-centered 3-dimensional descriptions starting from point-wise 3D range data obtained by a light-stripe rangefinder. A careful geometrical analysis shows that contours which appear in light-stripe range images can be classified into eight types, each with different characteristics in occluding vs occluded and different camera/illuminator relationships. Starting with detecting these contours in the iconic range image, the descriptions are generated moving up the hierarchy of contour, surface, object, to scene. We use conical and cylindrical surfaces as primitives. In this process, we exploit the fact that coherent relatinships, such as symmetry, and being coaxial, which are present among lower-level elements in the hierarchy allow us to hypothesize upper-level elements. The resultant descriptions are used for matching and recognizing objects. The analysis program has been applied to complex scenes containing cups, pans, and toy shovels.