Learning Occupancy Grid Maps with Forward Sensor Models

This article describes a new algorithm for acquiring occupancy grid maps with mobile robots. Existing occupancy grid mapping algorithms decompose the high-dimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. This induces conflicts that may lead to inconsistent maps, even for noise-free sensors. This article shows how to solve the mapping problem in the original, high-dimensional space, thereby maintaining all dependencies between neighboring cells. As a result, maps generated by our approach are often more accurate than those generated using traditional techniques. Our approach relies on a statistical formulation of the mapping problem using forward models. It employs the expectation maximization algorithm for searching maps that maximize the likelihood of the sensor measurements.     

Detecting Loop Closure with Scene Sequences

This paper is concerned with “loop closing” for mobile robots. Loop closing is the problem of correctly asserting that a robot has returned to a previously visited area. It is a particularly hard but important component of the Simultaneous Localization and Mapping (SLAM) problem. Here a mobile robot explores an a-priori unknown environment performing on-the-fly mapping while the map is used to localize the vehicle. Many SLAM implementations look to internal map and vehicle estimates (p.d.fs) to make decisions about whether a vehicle is revisiting a previously mapped area or is exploring a new region of workspace. We suggest that one of the reasons loop closing is hard in SLAM is precisely because these internal estimates can, despite best efforts, be in gross error. The “loop closer” we propose, analyze and demonstrate makes no recourse to the metric estimates of the SLAM system it supports and aids---it is entirely independent. At regular intervals the vehicle captures the appearance of the local scene (with camera and laser). We encode the similarity between all possible pairings of scenes in a “similarity matrix”. We then pose the loop closing problem as the task of extracting statistically significant sequences of similar scenes from this matrix. We show how suitable analysis (introspection) and decomposition (remediation) of the similarity matrix allows for the reliable detection of loops despite the presence of repetitive and visually ambiguous scenes. We demonstrate the technique supporting a SLAM system driven by scan-matching laser data in a variety of settings. Some of the outdoor settings are beyond the capability of the SLAM system itself in which case GPS was used to provide a ground truth. We further show how the techniques can equally be applied to detect loop closure using spatial images taken with a scanning laser. We conclude with an extension of the loop closing technique to a multi-robot mapping problem in which the outputs of several, uncoordinated and SLAM-enabled robots are fused without requiring inter-vehicle observations or a-priori frame alignment.     

Automatic Mapping of Dynamic Office Environments

We present a robot, InductoBeast, that greets a new office building by learning the floorplan automatically, with minimal human intervention and a priori knowledge. Our robot architecture is unique because it combines aspects of both abductive and inductive mapping methods to solve this problem. We present experimental results spanning three ofiice environments, mapped and navigated during normal business hours. We hope these results help to establish a performance benchmark against which robust and adaptive mapping robots of the future may be measured.     

Human navigation and mapping with a 6DOF IMU and a laser scanner

This article focuses on human navigation, by proposing a system for mapping and self-localization based on wearable sensors, i.e., a laser scanner and a 6 Degree-of-Freedom Inertial Measurement Unit (6DOF IMU) fixed on a helmet worn by the user. The sensor data are fed to a Simultaneous Localization And Mapping (SLAM) algorithm based on particle filtering, an approach commonly used for mapping and self-localization in mobile robotics. Given the specific scenario considered, some operational hypotheses are introduced in order to reduce the effect of a well-known problem in IMU-based localization, i.e., position drift. Experimental results show that the proposed solution leads to improvements in the quality of the generated map with respect to existing approaches.     

一套完整的基于视觉光流和激光扫描测距的室内无人机导航系统

提出了一套室内四旋翼无人机控制, 导航, 定位和地图构建的完整解决方案. 无人机机载系统包括三个主要传感器, 即惯性测量单元, 下视相机和激光扫描测距仪. 经过处理, 融合这些传感器的测量数据, 无人机能够可靠的估计自己的飞行速度和实时位置, 并且沿着室内的墙壁进行无碰撞飞行. 通过收集一个完整飞行实验的数据, 无人机的飞行路径和在室内的环境也可以被很好地估计出来. 这套系统中的自主导功能不需要任何远程传感信息或脱机计算能力. 这套室内导航方案的性能和可靠性已在实际的飞行实验中被验证.     

依靠自身传感器的室内无人机自主导航引导技术综述

具有广泛应用前景的微型无人机已成为各国学者的研究热点,而不依赖卫星导航系统的室内微型无人机自主导航引导技术是研究重点之一。结合近年国内外室内无人机自主导航引导技术发展情况,讨论了依靠自身传感器实现自主导航引导的关键技术问题,详细分析了无人机位姿的解算、无人机动态避障和同步定位与地图构建的关键技术的实现情况及其难点。最后,对室内无人机自主导航引导技术进行了展望。