AI for Robot Navigation: SLAM, Visual Odometry, and Learned Policies

Status: public · Confidence: medium (0.86) · Basis: verified_sources

## TL;DR

Robot navigation asks a robot to estimate where it is, understand nearby obstacles, and choose a route to a goal. AI contributes through learned depth, matching, motion estimation, and navigation policies, but deployment still depends on sensors, maps, validation, and safety constraints.

## Core Explanation

Classical visual SLAM estimates a robot or camera trajectory while building a map from visual observations. Visual-inertial variants combine camera images with inertial measurements to improve robustness. Learned SLAM methods add neural networks for correspondence, depth, or motion estimation, but still have to solve geometric consistency problems.

Path planning and learned navigation are related but separate. A robot may use SLAM to localize, then use a planner or learned policy to choose actions. Reinforcement-learning navigation research shows how policies can be trained in simulated environments, but real-world robots still need careful transfer testing, obstacle handling, and fail-safe behavior.

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