SLAM++: Simultaneous Localisation and Mapping at the Level of Objects

We present the major advantages of a new ‘object ori-ented ’ 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, real-time 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph opti-misation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruc-tion, but with a huge representation compression. The ob-ject graph enables predictions for accurate ICP-based cam-era to model tracking at each live frame, and efficient ac-tive search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relo-calisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction. 1

Object Registration in Semi-cluttered and Partial-occluded Scenes for Augmented Reality

This paper proposes a stable and accurate object registration pipeline for markerless augmented reality applications. We present two novel algorithms for object recognition and matching to improve the registration accuracy from model to scene transformation via point cloud fusion. Whilst the first algorithm effectively deals with simple scenes with few object occlusions, the second algorithm handles cluttered scenes with partial occlusions for robust real-time object recognition and matching. The computational framework includes a locally supported Gaussian weight function to enable repeatable detection of 3D descriptors. We apply a bilateral filtering and outlier removal to preserve edges of point cloud and remove some interference points in order to increase matching accuracy. Extensive experiments have been carried to compare the proposed algorithms with four most used methods. Results show improved performance of the algorithms in terms of computational speed, camera tracking and object matching errors in semi-cluttered and partial-occluded scenes

SAMSLAM: Simulated Annealing Monocular SLAM

Abstract. This paper proposes a novel monocular SLAM approach. For a triplet of successive keyframes, the approach inteleaves the registration of the three 3D maps associated to each image pair in the triplet and the refinement of the corresponding poses, by progressively limiting the allowable reprojection error according to a simulated annealing scheme. This approach computes only local overlapping maps of almost constant size, thus avoiding problems of 3D map growth. It does not require global optimization, loop closure and back-correction of the poses

Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment

Abstract. This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment

Brainstormers 2D - Team Description 2005

The main interest behind the Brainstormers' effort in the RoboCup soccer domain is to develop and to apply machine learning techniques in complex domains. In particular, we are interested in applying Reinforcement Learning methods, where the training signal is only given in terms of success or failure. Our final goal is a learning system, where we only plug in 'win the match' -- and our agents learn to generate the appropriate behavior. Unfortunately, even from very optimistic complexity estimations it becomes obvious, that in the soccer domain, both conventional solution techniques and also advanced today's Reinforcement Learning techniques come to their limit -- there are more than (10850)^23 different states and more than (1000)^300 different policies per agent per half time. This paper describes the architecture of the Brainstormers team, focuses on the use of Reinforcement Learning to learn various elements of out agents' behavior, and highlights other advanced artificial intelligence methods we are employing