7 accepted papers
Pose graph optimization (PGO) is a fundamental technique for robot localization. It is typically encoded with a sparse graph. The recent work on the cycle-based PGO reveals the merits of solving PGOs in the graph cycle space, which brings the computation of the minimum cycle basis (MCB) into the rob
Simultaneous localization and mapping (SLAM) in the deformable environment has encountered several barricades. One of them is the lack of a global registration technique. Thus current SLAM systems heavily rely on template based methods. We propose KernelGPA, a novel global registration technique to…
Cameras on portable devices are manufactured with a rolling-shutter (RS) mechanism, where the image rows (aka. scanlines) are read out sequentially. The unknown camera motions during the imaging process cause the so-called RS effects which are solved by motion assumptions in the literature. In this…
A variety of optimization problems takes the form of a minimum norm optimization. In this paper, we study the change of optimal values between two incrementally constructed least norm optimization problems, with new measurements included in the second one. We prove an exact equation to calculate the…
Embedded deformation graph is a widely used technique in deformable geometry and graphical problems. Although the technique has been transmitted to stereo (or RGB-D) camera based SLAM applications, it remains challenging to compromise the computational cost as the model grows. In practice, the proce…
Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outl…
In this letter, we propose a constrained optimization formulation and a robust incremental framework for the simultaneous localization and mapping problem (SLAM). The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles