# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 7 Conclusions and Future Works  
One of the major challenges in a robotic system that integrates multiple modules on a single modular framework, is the integration between the modules which are running on separate processing units. Latency in data transformation between different processing units decreases the performance of the system. Combining modules to run on the same processing unit can address this challenge and also reduces the cost, size, and weight of the system which counts as a significant improvement for mobile robotic systems that have either limited computing resources or payload capacity (e.g., unmanned aerial vehicles). This paper presents an efficient semantic segmentation model that can be run in real-time on multiple embedded platforms that are integrated as a system to determine navigable space in real-time. The results show improvement in model size, memory usage, and computation time (50% and 18% reduction in model size and inference time respectively) while keeping the accuracy almost the same. 50% reduction in the model size is a significant contribution, which enables multiple modules to be combined and run on the same processing unit. The core of model architecture is a new block based on separable convolution which compresses the parameters of present residual block meanwhile maintaining the accuracy and performance.  
The proposed method is a step forward in making intelligent and contextually aware robots ubiquitous. In the future, the model's segmentation ability and inference speed could be further improved by applying new neural network architectures and new loss function design. The proposed binary problem in the dataset is a good starting point but more complex classification tasks would be required in more realistic scenarios. So, a high-precise and multi-class annotated construction site dataset are in need. Moreover, existing 3D models (i.e., BIM) of construction sites can potentially guide and streamline the data collection of highly precise pixel-level annotations.  
Figure 10: Inference time per frame for ENet (blue frequency) and LNSNet (orange frequency) and corresponding linear regressions (blue and orange straight lines).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Mean (s)** & **Std (s)** & **df** & **\(t\)-Value** & **\(p\)-Value** \\ \hline ENet & 0.0447 & 0.0048 & & & \\ LNSNet & 0.0365 & 0.0042 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean, standard deviation, and \(t\)-test results on the inference times.  
**Author Contributions**: Conceptualization, K.A. and P.C. and K.H.; methodology, K.A. and P.C. and K.H.; software, K.A. and P.C.; validation, K.A. and P.C. and K.H.; formal analysis, K.A. and P.C.; investigation, K.A. and P.C.; resources, K.H. and T.W. and E.L.; data curation, K.A. and P.C.; writing--original draft preparation, K.A. and P.C. and K.H.; writing--review and editing, K.A. and P.C. and K.H. and T.W. and E.L.; visualization, K.A. and P.C. and K.H.; supervision, K.A. and K.H.; project administration, K.A. and K.H.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 Graphics Processing Units used for this research. Any opinions, findings, conclusions or recommendations presented in this paper are those of the authors and do not reflect the views of NVIDIA.  
**Conflicts of Interest**: The authors declare no conflicts of interest.