The current nonlinear optimization of visual SLAM back-end has disadvantages such as slow optimization speed and poor optimization effect. To overcome these problems, this paper improves the traditional Levenberg-Marquardt algorithm (L-M) based on a framework of Bundle Adjustment (BA) nonlinear optimization. Firstly, the radius and expansion multiplier of the trust region are formulated and a threshold value is set; secondly, the trust region after each iteration is restricted with the pre-defined range for the purpose of improving nonlinear optimization; finally, a comparative analysis is conducted by setting up a comparison experiment with the traditional L-M algorithm, and It is concluded that the improved L-M algorithm can reduce the number of iterations by 16 times and time performance was reduced by an average of 62.30%, which shorens the optimization time, improves the optimization efficiency and has better robustness.

Laser slam usually needs to complete a back-end graph optimization at a fast speed in some specific scenes, such as sharp turns, fast motion, and limited calculation time. Aiming at these problems, this paper proposed a 2D laser slam back-end graph optimization combined with Cholesky decomposition to accelerate a linear solution process and further to achieve a purpose of accelerating graph optimization. In MATLAB simulation experiments, the rate of 2D laser slam back-end graph optimization combined with Cholesky decomposition increased 24%, compared to that of the traditional method without Cholesky decomposition. The result verified the effectiveness of the improved method.

BACKGROUND: Small peptides encoded as one- or two-exon genes in plants have recently been shown to affect multiple aspects of plant development, reproduction and defense responses. However, popular similarity search tools and gene prediction techniques generally fail to identify most members belonging to this class of genes. This is largely due to the high sequence divergence among family members and the limited availability of experimentally verified small peptides to use as training sets for homology search and ab initio prediction. Consequently, there is an urgent need for both experimental and computational studies in order to further advance the accurate prediction of small peptides. RESULTS: We present here a homology-based gene prediction program to accurately predict small peptides at the genome level. Given a high-quality profile alignment, SPADA identifies and annotates nearly all family members in tested genomes with better performance than all general-purpose gene prediction programs surveyed. We find numerous mis-annotations in the current Arabidopsis thaliana and Medicago truncatula genome databases using SPADA, most of which have RNA-Seq expression support. We also show that SPADA works well on other classes of small secreted peptides in plants (e.g., self-incompatibility protein homologues) as well as non-secreted peptides outside the plant kingdom (e.g., the alpha-amanitin toxin gene family in the mushroom, Amanita bisporigera). CONCLUSIONS: SPADA is a free software tool that accurately identifies and predicts the gene structure for short peptides with one or two exons. SPADA is able to incorporate information from profile alignments into the model prediction process and makes use of it to score different candidate models. SPADA achieves high sensitivity and specificity in predicting small plant peptides such as the cysteine-rich peptide families. A systematic application of SPADA to other classes of small peptides by research communities will greatly improve the genome annotation of different protein families in public genome databases.