If I ask you, “Where are you now?’” or “What do your surroundings seem like?” you’ll immediately find a way to reply owing to a singular ability often called multisensory perception in humans that lets you perceive your motion and your surrounding environment ensuring you could have complete spatial awareness. But think as if the identical query is posed to a robot: how wouldn’t it approach the challenge? 

The problem is that if this robot doesn’t have a map, it cannot know where it’s, and if it doesn’t know what its surroundings seem like, neither can it create a map. Essentially, making this a ‘who got here first, chicken or egg?’ problem which within the machine learning world on this context is termed as a localization and mapping problem. 

“Localization” is the aptitude to amass internal system information related to a robot’s motion, including its position, orientation, and speed. However, “mapping” pertains to the flexibility to perceive external environmental conditions, encompassing facets corresponding to the form of the environment, their visual characteristics, and semantic attributes. These functions can operate independently, with one focused on internal states and the opposite on external conditions, or they’ll work together as a single system often called Simultaneous Localization and Mapping (SLAM).

The prevailing challenges with algorithms corresponding to image-based relocalization, visual odometry, and SLAM include imperfect sensor measurements, dynamic scenes, adversarial lighting conditions, and real-world constraints that somewhat hinder their practical implementation. The image above demonstrates how individual modules will be integrated right into a deep learning-based SLAM system. This piece of research presents a comprehensive survey on how deep learning-based approaches and traditional approaches and concurrently answers two essential questions:

1. Is deep learning promising for visual localization and mapping?

Researchers imagine three properties listed below could make deep learning a singular direction for a general-purpose SLAM system in the longer term. 

• First, deep learning offers powerful perception tools that will be integrated into the visual SLAM front end to extract features in difficult areas for odometry estimation or relocalization and supply dense depth for mapping. 
• Second, deep learning empowers robots with advanced comprehension and interaction capabilities. Neural networks excel at bridging abstract concepts with human-understandable terms, like labeling scene semantics inside a mapping or SLAM systems, that are typically difficult to explain using formal mathematical methods.
• Finally, learning methods allow SLAM systems or individual localization/mapping algorithms to learn from experience and actively exploit recent information for self-learning. 

2. How can deep learning be applied to resolve the issue of visual localization and mapping?

• Deep learning is a flexible tool for modeling various facets of SLAM and individual localization/mapping algorithms. As an illustration, it could be employed to create end-to-end neural network models that directly estimate pose from images. It is especially useful in handling difficult conditions like featureless areas, dynamic lighting, and motion blur, where conventional modeling methods may struggle.
• Deep learning is used to resolve association problems in SLAM. It aids in relocalization, semantic mapping, and loop-closure detection by connecting images to maps, labeling pixels semantically, and recognizing relevant scenes from previous visits.
• Deep learning is leveraged to find features relevant to the duty of interest robotically. By exploiting prior knowledge, e.g., the geometry constraints, a self-learning framework can robotically be arrange for SLAM to update parameters based on input images. 

It could be identified that deep learning techniques depend on large, accurately labeled datasets to extract meaningful patterns but can have difficulty generalizing to unfamiliar environments. These models lack interpretability, often functioning as black boxes. Moreover, localization and mapping systems will be computationally intensive while highly parallelizable unless model compression techniques are applied.

Take a look at the Paper. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to hitch our 29k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the most recent AI research news, cool AI projects, and more.

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