The primary goal of robot navigation and mapping is to enable robots to navigate their environment safely and efficiently, avoiding collisions with obstacles.

SLAM (Simultaneous Localization and Mapping) is a widely used approach for robot navigation in unknown environments, as it allows the robot to simultaneously build a map of its surroundings while estimating its own position within the map.

Path planning aims to find an optimal path for the robot to follow from its current position to a desired goal location, taking into account constraints such as obstacles and the robot's capabilities.

In indoor environments, GPS signals are often unavailable or unreliable. IMU (Inertial Measurement Unit) sensors, which measure the robot's acceleration and angular velocity, are commonly used for localization, as they provide continuous information about the robot's motion.

Outdoor environments are highly dynamic and unpredictable, with moving objects, changing weather conditions, and varying terrain. This poses significant challenges for robot navigation, as the robot needs to adapt to these changing conditions in real-time.

A* is a widely used algorithm for global path planning in robot navigation. It finds the shortest path from the start to the goal location by expanding nodes in the search space and evaluating their cost and heuristic values.

The primary goal of robot mapping is to create a detailed representation of the robot's environment, including obstacles, landmarks, and other features. This map can then be used for navigation, localization, and other tasks.

LiDAR (Light Detection and Ranging) sensors are commonly used for robot mapping in outdoor environments. They emit laser pulses and measure the time it takes for the pulses to reflect off objects, providing accurate and detailed 3D point cloud data of the surroundings.

Indoor environments are often narrow and cluttered, with furniture, walls, and other obstacles. This makes it challenging for robots to navigate and map these environments effectively.

EKF (Extended Kalman Filter) is a widely used algorithm for SLAM. It combines sensor measurements with a motion model to estimate the robot's pose and build a map of the environment simultaneously.

Particle filters are particularly useful in SLAM because they can handle non-Gaussian noise and uncertainties, which are common in real-world environments.

GPS (Global Positioning System) is commonly used for robot localization in outdoor environments. It provides accurate position information by receiving signals from multiple satellites.

Dynamic environments, such as crowded public spaces or busy roads, pose significant challenges for robot navigation, as the robot needs to adapt to changing conditions and avoid collisions with moving objects.

PRM (Probabilistic Road Map) is a widely used algorithm for local path planning in robot navigation. It generates a roadmap of randomly sampled points in the environment and connects them to create a graph, which can then be used to find a path from the start to the goal location.

RRT (Rapidly-exploring Random Tree) is particularly useful for path planning in non-convex environments, where traditional methods may fail to find a solution.