The AIS 3D Laser Range Finder

The AIS 3D laser range finder (figure 1) [19] is built on the basis of a 2D range finder by extension with a mount and a servomotor. The 2D laser range finder is attached to the mount for chieving a controlled pitch motion. A standard servo is connected on the left side (figure 1) and is controlled by the computer running RT-Linux, a real-time operating system which runs LINUX as a task with lowest priority [19,20]. The 3D laser scanner operates up to 5h (Scanner: 17 W, 20 NiMH cells with a capacity of 4500 mAh, Servo: 0.85 W, 4.5 V with batteries of 4500 mAh).

Figure 1: The AIS 3D laser range finder.

The area of $180^{\circ}$(h)$\times 120^{\circ}$(v) is scanned with different horizontal (181, 361, 721) and vertical (128, 256) resolutions. A plane with 181 data points is scanned in 13 ms by the 2D laser range finder (rotating mirror device). Planes with more data points, e.g., 361, 721, duplicate or quadruplicate this time. Thus a scan with 181 $\times$ 256 data points needs 3.4 seconds. In addition to the distance measurement the 3D laser range finder is capable of quantifying the amount of light returning to the scanner. Figure 2 (top row) shows an example of a reflectance image of the GMD-Robobench, a standard office environment for the evaluation of autonomous robots. The left image gives an distorted view of the scene: One scan line of the figure corresponds to a slice of the 2D scanner, the rotation of the scanner is not considered. The right image shows the scene with the distortions corrected.

Figure 2: Two persons standing in a corridor of an office building (GMD Robobench). Top left: Reflectance image (distorted). Top right: Corrected reflectance image with distant points clipped. Bottom left: All points. Bottom right: Result of line detection with orientation.

The basis of the scan matching are algorithms for reducing points and detecting lines. Next we give a brief description of these algorithms. Details can be found in [19,20].

The scanner emits the laser beams in a spherical way, such that the data points close to the source are more dense. The first step is to reduce the data. Therefore, data points located close together are joined into one point. The number of these reduced points is one order of magnitude smaller than before (figure 6 (right)). Furthermore noise within the data is reduced [20].

Second a simple length comparison is used as a line detection algorithm. Given that the anticlockwise ordered data of the laser range finder (points $a_0, a_1, \ldots, a_n$) are located on a line, then for $a_{j+1}$ the algorithm has to check if $\Vert a_i, a_{j+1} \Vert \/ \\ sum^j_{t=i}\Vert a_t,a_{t+1} \Vert < \epsilon(j)$ to determine if $a_{j+1}$ is on line with $a_j$.