# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 2 Related Work
### Recognition
#### 2.3.1 Object Recognition  
Visual object recognition is a significant problem in mobile robots. Advanced research efforts in object recognition [37; 38] are focused on understanding how a cognitive representation, from the filtered output of sensor data, can be achieved to empower the robot with human-like perception [39]. In [40], human-like object recognition was developed for information processing using three hypotheses based on cognitive psychology. Deep neural networks (DNNs) inspired by neurons in the brain have recently come out as powerful algorithms to investigate the biological vision. The neural basis of DNNs has motivated the researchers to use them as models for information processing for recognition tasks [41]. CNN-based object recognition models are categorized into two-stage and one-stage detectors, in which You Only Look Once (YOLO) [42] is considered extremely fast and performs the detection task as a regression problem. [PERSON]. [43], presented a robust method for object recognition based on a semantic model graph in which structural and geometrical attributes along with semantics as intrinsic properties of objects were used to establish a link between high-level semantics and low-level image features. [PERSON] et al. [44] proposed SPDA-CNN architecture consisting of two sub-networks and introduced mid-level layers for extracting part-specific features to improve the recognition performance. Detecting and recognizing partially occluded parts is a challenging task for modern object detectors because they cannot deal with occlusions. [PERSON] et al. [45] addressed this issue by accumulating the confidence of local visual cues, also known as visual concepts, which were extracted from the internal states of deep networks using clustering.  
Many researchers have considered the ontology-based knowledge-driven approaches for the semantic recognition task. In an early study, [PERSON] et al. [46] proposed an approach of ontology inference for constructing the semantic contexts extracted from robot sensors to perform object recognition tasks in real-world environment. Web Ontology Language (OWL) was used for the representation of object ontology in which semantic contexts were generated using axiomatic rules. Another study [47] represented a cognitive vision approach for recognizing the categories of complex objects. This approach was proposed for the semantic interpretation of individual objects. The visual concept of ontology was composed of spatial, color, and texture data. These concepts were used as an intermediate layer, while the link between symbols and sensory information was established using machine learning techniques. [PERSON] et al. [48] introduced a semantic information retrieval approach to build a modular ontology and graph-based model for visual features. A seminal study in this area was the Adaptive Bayesian Recognition framework introduced by [PERSON] et al. [49], which endowed the robot with semantic understanding of 3D objects in home environment. A novel FER-CNN model was incorporated with this framework to extract and reconstruct 3D features by semantically linking to the ontology.