Laser and Radar Based Robotic Perception

• Authors: Martin Adams;John Mullane;Ba-Ngu Vo
• Venue: Foundations and Trends in Robotics
• Year: 2011

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 aspects of the estimation theoretic algorithms for both approaches. Emphasis on the measurement likelihoods is used to incorporate measurement uncertainty, and their impact on the resulting stochastic formulations is examined. This paper also explores the front-ends of commonly used laser and radar sensors to develop an in-depth understanding of inherent measurement uncertainty. In this monograph, perceptive uncertainty is largely categorized into that related to signal detection and range measuring. While range noise is commonly addressed in the robotics literature, there is less emphasis placed on detection uncertainty and its subsequent impact on stochastic robotic perception algorithms. As such, following a signal level analysis of both laser and radar range finders, this paper addresses stochastic measurement modeling and map representations. In particular, occupancy grid methods based on spatial statistics are reviewed as well as those more recently based on detection statistics. Recent work, which proposes that the occupancy state space is more appropriately propagated by applying the discrete Bayes recursion using estimates of the detection and false alarm probabilities, as opposed to the commonly used range measurement likelihoods, is discussed. A review of FB perception methods is presented, with particular attention to the important fields of robotic mapping and SLAM. In particular, comparisons of state-of-the-art Gaussian, Gaussian mixture, and nonparametric map representations are given, demonstrating the assumptions and advantages of each technique. Finally, recent FB frameworks using random finite sets are reviewed in which the measurement model is generalized to include detection uncertainty and the feature map representation is generalized to incorporate uncertainty in the number of features present. These recent developments add a new direction to the well-studied problem of robotic perception and the estimation of any given environment.