标签：most rest pid view 启发式 ide arm sdn ping
启发式算法（heuristic algorithm)是相对于最优化算法提出的。一个问题的最优算法求得该问题每个实例的最优解。启发式算法可以这样定义：一个基于直观或经验构造的算法，在可接受的花费（指计算时间和空间）下给出待解决组合优化问题每一个实例的一个可行解，该可行解与最优解的偏离程度一般不能被预计。现阶段，启发式算法以仿自然体算法为主，主要有蚁群算法、模拟退火法、神经网络等
Abstract-we advocate a probabilistically-motivated scan-matching algorithm that produces higher quality and more robust results at the cost of additional computation time.
We describe several novel implementations of this approach that achieve real-time performance on modern hardware, including a multi-resolution approach for conventional CPUs, and a parallel approach for
graphics processing units (GPUs).
The robustness of the methods make them especially useful for global loop-closing.
1.Introduction
Aside from being an interesting perceptual problem, scan matching is at the center of most navigation, mapping, and localization systems.
scan matching plays a central role in estimating the motion of the robot.
The primary challenge in designing a scan matcher is to minimize the runtime complexity while maximizing the quality (and robustness) of the solutions.
This paper describes a family of scan matching algorithms based upon cross-correlation of two lidar scans. Our approach casts the problem in a probabilistic framework: it finds the
rigid-body transformation that maximizes the probability of having observed the data.
Rather than trusting a local search algorithm to find the global maximum (an approach that does not work well in the presence of initialization noise, as we will illustrate),
we perform a search over the entire space of plausible rigid-body transformations. This plausible region is derived from a prior which, in turn, can be derived from the commanded motion or wheel/visual odometry.
The quality and robustness of our methods, coupled with their ability to operate in real-time, make them ideal for any robotic platform in which robustness and accuracy are of high importance.
II-Prior Work
Iterative Closest Point (ICP) [1], [2] and Iterative Closest Line (ICL) [3], [4], [5] are used pervasively in scan matching.Horn’s exact closed-form algorithm [6] is especially well suited to the task.
Our work is quite similar in spirit to Konolige’s correlation based localization approach [15]. While the formulations of the problem are almost identical, we describe new methods for computing the answers.
III APPROACH
A Probabilistic formulation
The central contribution of this paper is a method for efficiently computing the distribution p(z|x i , m) so that we can compute the posterior distribution of the robot’s position
Our approach results in both a more robust maximum likelihood estimate and a principled uncertainty estimate.
Like previous work, we assume that each individual lidar return z j is independent,
The probability distribution for a single lidar sample z j should, in principle, consider which surface of the map m would be visible from position x i along a particular bearing. This would require an expensive ray-casting type operation. Like others [15], we neglect visibility and occlusion effects, and approximate the probability of z j in terms of its distance from any surface in m.
B Lookup-Table Rasterization
The computation of the probability p(z|x i , m) can be accelerated by building a 2D lookup table. We follow the approach of previous approaches [18], [19] by pre-computing a lookup table containing log probabilities of lidar observation at each(x, y) position in the world.
Our rasterization process begins with map m.For each observable point m i in the map, we can the compute the conditional probability that the sensor observes a nearby point p given that m i was the cause of that observation.(栅格化过程以地图m开始，对于在地图中的每一个可观测的点mi，我们可以计算传感器观测到附近点p在给定mi的条件下的条件概率，mi是这个观测的原因)。
We repeat this process for each point in the map, recording the maximum probability for each point in the lookup table.
Since our lookup table must be viewpoint independent, we approximate the potentially banana-shaped distribution arising from the sensor model (which has independent range and bearing noise) as a radially-symmetric distribution.
we approximate the potentially banana-shaped distribution arising from the sensor model (which has independent range and bearing noise) as a radially-symmetric distribution.(我们近似这个来自于传感器模型的香蕉形状的分布作为一个径向对称的分布)
The resulting lookup table can be visualized as an image, as seen in Fig. 3.
光栅化：http://blog.csdn.net/u010356727/article/details/50594401
http://www.cnblogs.com/wantnon/p/4894597.html
A critical question is: “where does the model m come from?”，In rare cases, a model is known in advance [10],but more often, the model must be estimated from previousobservations of the environment.Estimating this model is a complex issue on its own, and in the limit, requires a full solution to the SLAM problem.
In this paper, we will simply use an earlier laser scan (the reference scan) as our model. This approach has the advantages of being easy to implement, robust (in that model cannotdiverge due to earlier data association errors), and perhaps most pragmatically, serves as a baseline implementation aiding replication of our results. More sophisticated implementations can build up more detailed models by combining multiple scans (e.g. CARMEN’s Vasco), or by extrapolating continuous surfaces from lidar points [4].
C.Approach Overview
our goal is to estimate the distribution p(z|x i , m),we are interested not only in the maximum likelihood value of p(x i |...), but the distribution itself so that we can obtain a measure of uncertainty.
There is no simple expression for this distribution:it must be evaluated numerically. In the following two sections, we will describe two algorithms for rapidly evaluating the distribution p(z|x i , m) over many values of x i .
D.Multi-Level Resolution Implementation
Our approach reflects this, attempting to minimize the number of evaluations required while 1) characterizing the distribution over a large area and 2) precisely locating the maximum likelihood value. We describe our approach in three pieces: building from a naive implementation towards our multi-resolution approach.
1)
real-Time Correlative Scan Matching
标签：most rest pid view 启发式 ide arm sdn ping
原文地址：http://www.cnblogs.com/gary-guo/p/7380873.html
