Although laser rangefinder technologies have been around for decades in military, cartography, building, industrial and research applications it is only in recent years that more generally applicable and cheaper consumer grade laser range finder sensors have become available. This project investigates the possibilities and limitations of creating a mobile 360 degree, two-dimensional obstacle detection system using off-the-shelf available electronic components. Using a Lidar Lite 3 from Garmin Ltd., an Arduino compatible microcontroller based on Atmel 328P, a Raspberry Pi 3 from The Raspberry Pi Foundation and an electronic speed controlled brushless DC motor driving the rotation, it is shown how range data measurements can be collected, communicated, processed and displayed at measurement rates between 500 and 1000 Hz. At 5 Hz update rate of a complete 360-degree data set, this translates to a worst case angular resolution of 2.5 degrees at ranges reaching 10 meters depending on target reflectivity. Configured for these faster measurement rates, at static measurements of a white painted wall, the measurements show a standard deviation of 0.06 m at a five-meter range, going up to 0.19 m at a range of 10 meters. A modular and mobile prototype was designed and built. The modularity allowed testing and verification of two configurations. Configuration A uses a slip ring for power and data transfer to the rotating sensor. Configuration B allows the laser range finder to be stationary and instead rotates a first surface aluminum mirror positioned at 45 degrees above the sensor. The measurement results show that increasing range has a notable adversely effect on the number of successful readings in a setting demanding faster measurement rates of above 500 Hz. The number of successful readings decreases at ranges above 5 meters, and this decrease of successful readings is more pronounced in the configuration using a mirror to reflect the measurement. The mirror reflected version does on the other hand allow an electromechanically simpler, more silent and durable system. Using a density based clustering algorithm it is shown how person sized objects in the point cloud data can be robustly detected at ranges up to 5 meters.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-328913 |
| Date | January 2017 |
| Creators | Ask, Simon, Lindh, Rickard |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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