Mapping Walls of Indoor Environment using RGB-D Sensor
This work addresses robot navigation challenges in indoor settings, but appears incremental as it builds on existing wall detection methods with a focus on real-time operation.
The paper tackles the problem of inferring wall configurations from moving RGB-D sensors to improve robot navigation in indoor environments, achieving real-time performance without requiring Manhattan World assumptions.
Inferring walls configuration of indoor environment could help robot "understand" the environment better. This allows the robot to execute a task that involves inter-room navigation, such as picking an object in the kitchen. In this paper, we present a method to inferring walls configuration from a moving RGB-D sensor. Our goal is to combine a simple wall configuration model and fast wall detection method in order to get a system that works online, is real-time, and does not need a Manhattan World assumption. We tested our preliminary work, i.e. wall detection and measurement from moving RGB-D sensor, with MIT Stata Center Dataset. The performance of our method is reported in terms of accuracy and speed of execution.