MAD-C: Multi-stage Approximate Distributed Cluster-combining for obstacle detection and localization
Journal article, 2021
The upcoming digitalization in the context of Cyber-physical Systems (CPS), enabled through Internet-of-Things (IoT) infrastructures, require efficient methods for distributed processing of the data, that is generated by multiple sources. We address the problem of obstacle detection and localization through data clustering, which is a common component for data processing in the fusion of multiple point clouds, each obtained by a LIDAR sensor. Such sensors generate data at high rates and can rapidly exhaust traditional methods that centrally gather and process the global data. To that end, we propose MAD-C, an approximate method for distributed data summarization through clustering, that can orthogonally build on known methods for fine-grained point-cloud clustering, and synthesize a decentralized approach, which exploits the distributed processing capacity efficiently and prevents saturation of the communication network. In MAD-C, corresponding to the point-cloud gathered by each LIDAR sensor, local clusters are first identified, each corresponding to an object in the sensed environment from the perspective of the respective sensor. Afterwards, the information about each locally detected object is transformed into a data-summary, computable in a continuous manner, with constant overhead in time and space. The summaries are then combined, in an order-insensitive, concurrent fashion, to produce approximate volumetric representations of the objects in the fused data. We show that the combined summaries, in addition to localizing objects and approximating their volumetric representations, can be used to answer relevant queries regarding the relative position of the objects in environment and a geofence. We evaluate the performance of MAD-C extensively, both analytically and empirically. The empirical evaluation is performed on an IoT test-bed as well as in simulation. Our results show that MAD-C leads to (i) communication savings proportional to the number of points, (ii) multiplicative decrease in the dominating component of the processing complexity and, at the same time, (iii) high accuracy (with Randlndex > 0.95), in comparison to its baseline counterpart for obstacle detection and localization, as well as (iv) linear computational complexity in terms of the number of objects, for the geofence related queries.