Laser and Radar Based Robotic Perception

Perceptive laser and radar sensors provide information from the surrounding environment and are a critical aspect of many robotics applications. These sensors are generally subject to many sources of uncertainty, namely detection and data association uncertainty, spurious measurements, biases as well as measurement noise. To deal with such uncertainty, probabilistic methods are most widely adopted. These probabilistic environmental representations, for autonomous navigation frameworks with uncertain measurements, can generally be subdivided into two main categories — grid based (GB) and feature based (FB). GB approaches are popular for robotic exploration, obstacle avoidance and path planning, whereas FB maps, with their reduced dimensionality, are primarily used for large scale robotic navigation and simultaneous localization and map building (SLAM). While researchers commonly distinguish both approaches based on their environmental representations, this paper examines the fundamental, theoretical Full text available at: http://dx.doi.org/10.1561/2300000011