Motilal Agrawal

FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping

Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers of point features, using classic bundle adjustment techniques from computational vision, but we keep only relative frame pose information (a skeleton). The skeleton is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration. We illustrate the workings of the system with large outdoor datasets (10 km), showing large-scale loop closure and precise localization in real time.

CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching

We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.

Large-Scale Visual Odometry for Rough Terrain

Motion estimation from stereo imagery, sometimes called visual odometry, is a well-known process. However, it is difficult to achieve good performance using standard techniques. We present the results of several years of work on an integrated system to localize a mobile robot in rough outdoor terrain using visual odometry, with an increasing degree of precision. We discuss issues that are important for real-time, high-precision performance: choice of features, matching strategies, incremental bundle adjustment, and filtering with inertial measurement sensors. Using data with ground truth from an RTK GPS system, we show experimentally that our algorithms can track motion, in off-road terrain, over distances of 10 km, with an error of less than 10 m (0.1%).

Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS

We describe a real-time, low-cost system to localize a mobile robot in outdoor environments. Our system relies on stereo vision to robustly estimate frame-to-frame motion in real time (also known as visual odometry). The motion estimation problem is formulated efficiently in the disparity space and results in accurate and robust estimates of the motion even for a small-baseline configuration. Our system uses inertial measurements to fill in motion estimates when visual odometry fails. This incremental motion is then fused with a low-cost GPS sensor using a Kalman filter to prevent long-term drifts. Experimental results are presented for outdoor localization in moderately sized environments (ges100 meters)

Outdoor Mapping and Navigation Using Stereo Vision

We consider the problem of autonomous navigation in an unstructured outdoor environment. The goal is for a small outdoor robot to come into a new area, learn about and map its environment, and move to a given goal at modest speeds (1 m/s). This problem is especially difficult in outdoor, off-road environments, where tall grass, shadows, deadfall, and other obstacles predominate. Not surprisingly, the biggest challenge is acquiring and using a reliable map of the new area. Although work in outdoor navigation has preferentially used laser rangefinders [14,2,6], we use stereo vision as the main sensor. Vision sensors allow us to use more distant objects as landmarks for navigation, and to learn and use color and texture models of the environment, in looking further ahead than is possible with range sensors alone.

Fast color/texture segmentation for outdoor robots

We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images. We present results of applying our descriptor for segmenting a synthetic image and compare it against other state-of-the-art descriptors. We also apply our segmentation algorithm to the task of detecting natural paths in outdoor images. The whole system has been demonstrated to work online alongside localization, 3D obstacle detection, and planning.

Real-Time Detection of Independent Motion using Stereo

We describe a system that detects independently moving objects from a mobile platform in real time using a calibrated stereo camera. Interest points are first detected and tracked through the images. These tracks are used to obtain the motion of the platform by using an efficient three-point algorithm in a RANSAC framework for outlier detection. We use a formulation based on disparity space for our inlier computation. In the disparity space, two disparity images of a rigid object are related by a homography that depends on the objects euclidean rigid motion. We use the homography obtained from the camera motion to detect the independently moving objects from the disparity maps obtained by an efficient stereo algorithm. Our system is able to reliably detect the independently moving objects at 16 Hz for a 320 x 240 stereo image sequence using a standard laptop computer.

Localization and Mapping for Autonomous Navigation in Outdoor Terrains : A Stereo Vision Approach

We consider the problem of autonomous navigation in unstructured outdoor terrains using vision sensors. The goal is for a robot to come into a new environment, map it and move to a given goal at modest speeds (1 m/sec). The biggest challenges are in building good maps and keeping the robot well localized as it advances towards the goal. In this paper, we concentrate on showing how it is possible to build a consistent, globally correct map in real time, using efficient precise stereo algorithms for map making and visual odometry for localization. While we have made advances in both localization and mapping using stereo vision, it is the integration of the techniques that is the biggest contribution of the research. The validity of our approach is tested in blind experiments, where we submit our code to an independent testing group that runs and validates it on an outdoor robot

A Lie Algebraic Approach for Consistent Pose Registration for General Euclidean Motion

We study the problem of registering local relative pose estimates to produce a global consistent trajectory of a moving robot. Traditionally, this problem has been studied with a flat world assumption wherein the robot motion has only three degrees of freedom. In this paper, we generalize this for the full six-degrees-of-freedom Euclidean motion. Given relative pose estimates and their covariances, our formulation uses the underlying Lie algebra of the Euclidean motion to compute the absolute poses. Ours is an iterative algorithm that minimizes the sum of Mahalanobis distances by linearizing around the current estimate at each iteration. Our algorithm is fast, does not depend on a good initialization, and can be applied to large sequences in complex outdoor terrains. It can also be applied to fuse uncertain pose information from different available sources including GPS, LADAR, wheel encoders and vision sensing to obtain more accurate odometry. Experimental results using both simulated and real data support our claim

Leaving Flatland: Toward real-time 3D navigation

We report our first experiences with Leaving Flatland, an exploratory project that studies the key challenges of closing the loop between autonomous perception and action on challenging terrain. We propose a comprehensive system for localization, mapping, and planning for the RHex mobile robot in fully 3D indoor and outdoor environments. This system integrates Visual Odometry-based localization with new techniques in real-time 3D mapping from stereo data. The motion planner uses a new decomposition approach to adapt existing 2D planning techniques to operate in 3D terrain. We test the map-building and motion-planning subsystems on real and synthetic data, and show that they have favorable computational performance for use in high-speed autonomous navigation.