The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we provide a practical algorithm for performing DATMO from a moving platform equipped with range sensors. The probabilistic approach to solve the whole problem has been implemented with the Navlab11 vehicle. More than 100 miles of experiments in crowded urban areas indicated that SLAM with DATMO is indeed feasible.

Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves both simultaneous localization and mapping (SLAM) in dynamic environments and detecting and tracking these dynamic objects. In this paper, we establish a mathematical framework to integrate SLAM and moving object tracking. We describe two solutions: SLAM with generalized objects, and SLAM with detection and tracking of moving objects (DATMO). SLAM with generalized objects calculates a joint posterior over all generalized objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modeling of generalized objects. Unfortunately, it is computationally demanding and generally infeasible. SLAM with DATMO decomposes the estimation problem into two separate estimators. By maintaining separate posteriors for stationary objects and moving objects, the resulting estimation problems are much lower dimensional then SLAM with generalized objects. Both SLAM and moving object tracking from a moving vehicle in crowded urban areas are daunting tasks. Based on the SLAM with DATMO framework, we propose practical algorithms which deal with issues of perception modeling, data association, and moving object detection. The implementation of SLAM with DATMO was demonstrated using data collected from the CMU Navlab11 vehicle at high speeds in crowded urban environments. Ample experimental results shows the feasibility of the proposed theory and algorithms. 1

Abstract — This paper presents an approach to generating compact 3D maps of urban environments using mobile robots and laser range finders. Our algorithm extracts planar information from 3D point cloud maps. The planar representation is very efficient for representing building structures in urban environments when a high level of detail is not required. We also present preliminary results on 3D geometric mapping with incomplete data. Based on previously known models and incomplete data, our system is able to estimate parts of buildings which have never been seen before. As validation we present experimental results using a Segway RMP vehicle in two environments, both approximately the size of a city block. I.

Precise realistic models of outdoor environments such as cities and roads are useful for various applications. However, for a high level of detail, and a large size of environment to be digitized, one has to face the issues of quantity of information to be acquired, processed and stored, and of overall processing time. We present in this paper an integrated on-board laser range sensing system addressing this need: it is designed to perform city and road geometric modelling as it moves. It is based on a laser range sensor mounted on a vehicle whose position is known trough GPS-INS localization; it produces raw 3D range data and performs specific modelling for cities and features extraction for roads. 1.

Robotic navigation requires that the robotic platform have an idea of its location and orientation within the environment. This localization is known as pose estimation, and has been a much researched topic. There are currently two main categories of pose estimation techniques: pose from hardware, and pose from video (PfV). Hardware pose estimation utilizes specialized hardware such as Global Positioning Systems (GPS) and Inertial Navigation Systems (INS) to estimate the position and orientation of the platform at the specified times. PfV systems use video cameras to estimate the pose of the system by calculating the inter-frame motion of the camera from features present in the images. These pose estimation systems are readily integrated, and can be used to augment and/or supplant each other according to the needs of the application. Both pose from video and hardware pose estimation have their uses, but each also has its degenerate cases in which they fail to provide reliable data. Hardware solutions can provide extremely accurate data, but are usually quite pricey and can be restrictive in their environments of operation. Pose from video solutions can be implemented with low-cost off-the-shelf components, but the accuracy of the PfV results can be degraded by noisy imagery, ambiguity in the feature matching process, and moving objects. This paper attempts to evaluate the cost/benefit comparison between pose from video and hardware pose estimation experimentally, and to provide a guide as to which systems should be used under certain scenarios.

Three-dimensional models of real world terrain have application in a variety of tasks, but digitizing a large environment poses constraints on the design of a 3D scanning system. We have developed a Mobile Scanning System that works within these constraints to quickly digitize large-scale real world environments. We utilize a mobile platform to move our sensors past the scene to be digitized – fusing the data from cm-level accuracy laser range scanners, positioning and orientation instruments, and high-resolution video cameras – to provide the mobility and speed required to quickly and accurately model the target scene.

Abstract — In this paper, we propose a novel type of 3-D

Mapping is a basic capability for mobile robots. Most applications demand some level of knowledge about the environment to be accomplished. Most mapping approaches in the literature are designed to perform in small structured (indoor) environments. This paper addresses the problems of localization and mapping in large urban (outdoor) environments. Due to their complexity, lack of structure and dimensions, urban environments presents several difficulties for the mapping task. Our approach has been extensively tested and validated in realistic situations. Our experimental results include maps of several city blocks and a performance analysis of the algorithms proposed. Keywords: Mobile robotics, Mapping, and Localization. 1.

A fast 3D model reconstruction methodology is desirable in many applications such as urban planning, training, and simulations. In this paper, we develop an approach for fast, automated 3D modeling of large scale urban environments based on airborne data. Since airborne data acquisition is considerably faster than ground based collection, our proposed methodology can scale to very large regions. At the core of our approach lies an automated algorithm for texture mapping oblique aerial images onto a 3D model generated from airborne Light Detection and Ranging (Li-DAR) data. Our proposed texture mapping algorithm consists of two steps. In the first step, we combine vanishing points and global positioning system aided inertial system readings to roughly estimate the extrinsic parameters of a calibrated camera. In the second step, we refine the coarse estimate of the first step by applying a series of processing steps. Specifically, We extract 2D orthogonal corners (2DOCs) corresponding to orthogonal 3D structural corners as features from both images and the untextured 3D LiDAR model. The correspondence between an image and the 3D model is then performed using Hough transform and generalized M-estimator sample consensus. The resulting 2DOC matches are used in Lowes algorithm to refine camera parameters obtained earlier. Our system achieves 91 % correct pose recovery rate for 90 images over the downtown Berkeley area, and overall 61 % accuracy rate for 358 images over the residential, downtown and campus portions of the city of Berkeley.