autonomous navigation编辑历史

## Overview

Autonomous navigation refers to the ability of a vehicle, robot, or other mobile system to determine its own position and plan a path through an environment without continuous human intervention. It integrates sensors (e.g., GPS, LiDAR, cameras, inertial measurement units), localization and mapping algorithms (e.g., SLAM), and decision-making software to perceive surroundings, avoid obstacles, and reach a designated destination. Autonomous navigation is a core capability in self-driving cars, drones, maritime vessels, and mobile robots deployed in logistics, agriculture, and exploration.

## 1 Core Concepts

### 1.1 Localization

Localization is the process by which a mobile system determines its position and orientation within a known or unknown coordinate frame. Accurate localization is fundamental to all subsequent navigation tasks.

#### 1.1.1 Global Positioning Systems (GPS)

GPS uses a constellation of satellites to provide absolute geographic coordinates (latitude, longitude, altitude). In open outdoor environments, GPS offers global coverage with meter‑level accuracy in standard mode. Differential GPS (DGPS) and Real‑Time Kinematic (RTK) techniques improve accuracy to centimeter level by correcting signal errors using fixed ground stations.

#### 1.1.2 Dead Reckoning and Inertial Navigation

Dead reckoning estimates position by integrating velocity and heading over time from a known starting point. Inertial navigation systems (INS) use accelerometers and gyroscopes to measure linear acceleration and angular velocity. While INS does not rely on external signals, it suffers from drift due to sensor noise and requires periodic correction from other sources (e.g., GPS or visual odometry).

#### 1.1.3 Simultaneous Localization and Mapping (SLAM)

SLAM solves the problem of building a map of an unknown environment while simultaneously tracking the robot’s location within that map. It uses sensor data (LiDAR, camera, or both) and probabilistic fi