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Context: The KITTI dataset [2] contains stereo sequences recorded from a car in urban and highway environments. The stereo sensor has a ∼54cm baseline and works at 10Hz with a resolution after rectification of 1240 × 376 pixels. Sequences 00, 02, 05, 06, 07 and 09 contain loops. Our ORB-SLAM2 detects all loops and is able to reuse its map afterwards, except for sequence 09 where the loop happens in very few frames at the end of the sequence. Table I shows results in the 11 training sequences, which have public ground-truth, compared to the state-of-the-art Stereo LSD-SLAM [11], to our knowledge the only stereo SLAM showing detailed results for all sequences. We use two different metrics, the absolute translation RMSE tabs proposed in [3], and the average relative translation trel and rotation rrel errors proposed in [2].

Question: What metrics are used for the evaluation of SLAM systems?
for evaluation of SLAM systems two different metrics, the absolute translation RMSE tabs proposed in [3], and the average relative translation trel and rotation rrel errors are used.