# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 1 Introduction  
In the past decade, the construction industry has struggled to improve its productivity while the manufacturing industry has experienced a dramatic productivity improvement [1]. The deficiency of advanced automation in construction is one possible reason [2]. On the other hand, construction progress monitoring has been recognized as one of the key elements that lead to the success of a construction project [3; 4]. Although there were various attempts by researchers to automate construction progress monitoring [5; 6; 7; 8; 9], in the present state, the monitoring task is still performed by site managers through on-site data collection and analysis, which are time-consuming and prone to errors [10]. If automated, the time spent on data collection can be better spent by the project management team, responding to any progress deviations by making timely and effective decisions.  
The use of Unmanned ground and aerial Vehicles (UVs) on construction sites can potentially automate the data collection required for visual data analytics that will automate the progress inference process. Moreover, the use of UVs has dramatically grown in the past few years [11]. To effectivelyutilize a UV for construction progress monitoring, it should (1) autonomously navigate, (2) collect multiple types of sensory and visual data, and (3) process them in real-time. Moreover, the lower computational complexity of a UV platform makes it more affordable and reliable to operate compared to other systems [12].  
The authors' previous research on an integrated mobile robotic system [13] presents an unmanned ground vehicle (UGV) that runs multiple modules and enables future development of an autonomous UGV. The platform was built on the Clearpaths Husky mobile robotic platform [14] by incorporating a stack of NVIDIA Jetson TX1 boards (denoted as Jetson boards) [15] and a monocular camera (i.e., a webcam) as a visual sensor. The Jetson boards are low-power embedded devices with integrated Graphics Processing Units (GPUs). In [13], they are used to process simultaneous localization and mapping, motion planning and control, and context awareness (via semantic image segmentation) modules of the system as illustrated in Figure 1.  
The two major bottlenecks of this platform, in terms of computational loads, were SLAM and segmentation. For this reason, there was a designated Jetson board for each of these tasks. This is a major challenge in developing an autonomous robot because it increases the size and weight, especially with added batteries. Moreover, the problem becomes even more challenging if this robotics system was to be applied to an unmanned aerial vehicle (UAV).  
This previous work implemented ENet [16] as the semantic segmentation method. This segmentation task (shown as Context Awareness Module in the red box in Figure 1) had the heaviest computational load. The inference speed for the ENet implementation on the Jetson TX1, before integration between multiple modules (SLAM, Control, and Context-Awareness each running on a Jetson), for images with the resolution of \(512\times 256\), was around 15 fps. After integration via a wired network the inference rate reduced to 3 fps. Data transformation between modules over the network using Robotic Operation System (ROS) [17] caused this slowdown which made the authors restrict the speed of the UGV for real-time performance necessity. Combining the SLAM and Context-Awareness Modules into the same Jetson TX1 would help to mitigate this problem, but the large size of the  
Figure 1: Overview of the previous research’s system components. Control, Simultaneous Localization and Mapping (SLAM), Context Awareness, and Mapping Modules adapted from ([PERSON] et al., 2018).  
segmentation model and the high memory usage, make this solution practically unfeasible. Improving the segmentation model in order to reduce the computational load in the Context Awareness Module will address this issue.