1D LIDAR Speed and Motion for the Internet-of-Things : For Railroad Classification Yards
Abstract: This thesis is an investigation into the feasibility of one-dimensional Light Detection and Ranging (LIDAR) sensors for tracking the position and motion of trains on railroad classification yards. Carefully monitoring railway traffic in these areas is important, in order to avoid accidents, optimise logistical operations and hence reduce delays. However, existing technologies for tracking trains on regular stretches of train-line, including Radio Frequency Identification (RFID) and Global Positioning System (GPS), have various drawbacks when applied to classification yards. As such, it is pertinent to investigate the extent to which simple LIDAR sensors could be used for this purpose, as part of a basic Internet of Things (IoT) system. To tackle this problem, we considered different ways of positioning the sensors around railway tracks. We then proposed a floating average algorithm for calculating a target object’s velocity using continuous LIDAR distance readings. To know when to apply the algorithm as a train is passing the sensor, we observed how the distance readings varied as a model train passed the sensor. The data was used to construct a Finite-state machine (FSM) that can fully describe the status of trains as they pass the sensor. In order to test our solution, we constructed a prototype sensor node implementing the FSM and evaluated its performance first with a model train and then on actual commuter trains on an outdoors train platform. We found that one-dimensional LIDAR sensors could feasibly be deployed to monitor the position and motion of trains with a high degree of consistency and accuracy. However, LIDAR may need to be corroborated with other types of technology such as RFID so that trains can be distinguished from other moving objects.
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