SLAM with objects using a nonparametric pose graph

Abstract

Abstract-Mapping and self-localization in unknown environments are fundamental capabilities in many robotic applications. These tasks typically involve the identification of objects as unique features or landmarks, which requires the objects both to be detected and then assigned a unique identifier that can be maintained when viewed from different perspectives and in different images. The data association and simultaneous localization and mapping (SLAM) problems are, individually, well-studied in the literature. But these two problems are inherently tightly coupled, and that has not been well-addressed. Without accurate SLAM, possible data associations are combinatorial and become intractable easily. Without accurate data association, the error of SLAM algorithms diverge easily. This paper proposes a novel nonparametric pose graph that models data association and SLAM in a single framework. An algorithm is further introduced to alternate between inferring data association and performing SLAM. Experimental results show that our approach has the new capability of associating object detections and localizing objects at the same time, leading to significantly better performance on both the data association and SLAM problems than achieved by considering only one and ignoring imperfections in the other.

Constructing Category-Specific Models for Monocular Object-SLAM

“…We estimate each object's shape (Λ) and pose (T Om i ) in each frame i by alternating minimization of the error term in (5), with respect to the pose and shape parameters. If the same object has been associated successfully across multiple frames, we also exploit temporal consistency [15] for more precise estimates.…”

“…Recognizing and keeping track of objects in a scene will enable a robot to build meaningful maps and scene descriptions. Object-SLAM is a relatively new paradigm [3]- [5] towards achieving this goal. Summarized in one sentence, object-SLAM attempts to augment SLAM with object information so that robot localization, object location estimation (in some cases, object pose estimation too), and mapping are achieved in a unified framework.…”

Compound locomotion control system combining crawling and walking for multi-crawler multi-arm robot to adapt unstructured and unknown terrain

“…The second one is to develop a method to explore the necessary terrain conditions by only using few vision sensors information. Laser range sensors are useful to obtain terrain information and robot position, which is called simultaneous localization and mapping (SLAM) system [12][13][14]. SLAM systems can describe the positional and simple postural relationship between the whole robot (robot body) and environment.…”

3D modeling of underground objects with the use of SLAM technology on the example of historical mine in Ciechanowice (Ołowiane Range, The Sudetes)

“…Implementation of the SLAM technology in positioning allows collecting the measurement data in parallel with the navigation data [12]. Kalman filtering [13] allows integrating LiDAR (Light Detection and Ranging) observations with the IMU (Inertial Measurement Units) data.…”