Prompt:

I am developing a SLAM system where keyframes are selected based on frame intervals and transformations (translation and rotation). I’m encountering an issue when varying the frame_interval. For example, when frame_interval=30, during the processing of frame 150, the system incorrectly selects frame 11 as a keyframe, which creates a significant gap. I believe the issue is rooted in how the system handles keyframe selection relative to both frame intervals and the logic that governs the minimum keyframe distance.

Keyframe selection code:
python
Kodu kopyala
def is_keyframe(self, frame_index, current_pose):
    if not self.keyframes:
        return True  # Always select the first frame as a keyframe

    frames_since_last_keyframe = frame_index - self.last_keyframe_index
    if frames_since_last_keyframe < self.min_keyframe_distance:
        return False

    last_keyframe = self.keyframes[-1]
    last_pose = last_keyframe['pose']

    # Calculate translation and rotation between current pose and last keyframe
    translation = np.linalg.norm(current_pose[:3, 3] - last_pose[:3, 3])
    rotation = np.degrees(np.arccos((np.trace(current_pose[:3, :3] @ last_pose[:3, :3].T) - 1) / 2))

    return translation > self.min_translation or rotation > self.min_rotation
The keyframe selection depends on:

A minimum distance of 10 frames (min_keyframe_distance).
A translation threshold of 2 meters (min_translation).
A rotation threshold of 20 degrees (min_rotation).
Problems:
Frame interval mismatch: The current keyframe selection doesn’t seem to fully account for the frame_interval. It selects keyframes based on the pose and frame number but ignores the gaps introduced by skipping frames (i.e., only processing every 30th frame). This mismatch could be why, even at frame 150, a distant frame like 11 is still considered a keyframe.

I think the keyframe logic should take into account that the system processes frames at intervals (e.g., frame_interval=30), so when checking frames_since_last_keyframe, it should consider frames in terms of the number of processed frames, not absolute frame indices.

Scale drift and pose estimation failure: Although you are calculating the scale using the depth maps, this could lead to drift if the scaling factor varies too widely between frames. You have some scale limiting logic (np.clip), but it’s worth considering if the system’s pose estimation can handle cases where scale is frequently clipped. This might cause jumps or inaccuracies in your pose graph, especially when intervals between processed frames increase. The reliance on depth map percentile scaling might need more robust handling, particularly in scenes with inconsistent depth data.

Feature matching failure: The SLAM pipeline relies heavily on successful ORB feature detection and matching. You currently handle cases where insufficient matches are found, but there is no fallback mechanism for such cases. If no keyframe is added when feature matching fails, it could leave long stretches of frames without reliable keyframe information, which could further contribute to gaps in your pose graph.

Pose graph anomalies: The keyframe selection depends on pose translation and rotation. However, if the pose graph accumulates errors over time (due to scaling issues or poor feature matches), even the best keyframe selection logic might not prevent divergence in your global map. Have you considered more sophisticated mechanisms for periodic re-optimization of the entire pose graph or realigning frames based on loop closure detection? This could help maintain consistency when using larger frame intervals.

Questions:
Should the keyframe selection logic account for frame_interval directly, perhaps by weighting frames processed at longer intervals more heavily in the decision to add a keyframe?
Would improving the pose estimation step with better handling of failed matches or more robust feature detection mitigate these issues?
Are there improvements to the depth scaling method that could help reduce drift over longer periods of SLAM processing?
How can I modify the code to fix these issues and ensure that the keyframe selection, pose estimation, and frame processing logic work harmoniously when using different frame intervals (e.g., frame_interval=30)? I’m especially interested in a solution that minimizes drift and improves robustness when skipping frames.

This version breaks down the problem comprehensively and highlights possible weak points that could affect your SLAM system. It also invites the model to think more deeply about the interconnected issues.