Geometric navigation is nowadays a well-established field of robotics and the
research focus is shifting towards higher-level scene understanding, such as
Semantic Mapping. When a robot needs to interact with its environment, it must
be able to comprehend the contextual information of its surroundings. This work
focuses on classifying and localising objects within a map, which is under
construction (SLAM) or already built. To further explore this direction, we
propose a framework that can autonomously detect and localize predefined
objects in a known environment using a multi-modal sensor fusion approach
(combining RGB and depth data from an RGB-D camera and a lidar). The framework
consists of three key elements: understanding the environment through RGB data,
estimating depth through multi-modal sensor fusion, and managing artifacts
(i.e., filtering and stabilizing measurements). The experiments show that the
proposed framework can accurately detect 98% of the objects in the real sample
environment, without post-processing, while 85% and 80% of the objects were
mapped using the single RGBD camera or RGB + lidar setup respectively. The
comparison with single-sensor (camera or lidar) experiments is performed to
show that sensor fusion allows the robot to accurately detect near and far
obstacles, which would have been noisy or imprecise in a purely visual or
laser-based approach.

提出一个利用多模态传感器融合的方法，能够在一个已知环境中，通过理解该环境搭配环境地图，准确地检测和定位预设物体和障碍物，并相比于单一的传感器能够更准确地检测远处和近处的障碍物。